{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/image-generation/papers/22","list_of":"/task/image-generation","task":"Image Generation","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":22,"pages_in_order":67,"rows_per_page":100,"rows":[2101,2200],"of":6689,"counts":{"archive_papers_tagged":6689,"with_a_code_link":3102,"where_syntology_ran_a_sample":1223,"not_listed_spam_title":0,"listed":6689,"listed_where_code_ran":1223,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1063,"every_run_a_failure_of_syntologys_instrument":160,"listed_with_a_run_with_no_instrument_failure":1063,"listed_every_run_a_failure_of_syntologys_instrument":160,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/image-generation","prev":"/task/image-generation/papers/21","next":"/task/image-generation/papers/23","papers":[{"url":"/paper/videofactory-swap-attention-in-spatiotemporal","slug":"videofactory-swap-attention-in-spatiotemporal","title":"Swap Attention in Spatiotemporal Diffusions for Text-to-Video Generation","date":"2023-05-18","arxiv_id":"2305.10874","repositories_listed":1,"syntology":null},{"url":"/paper/x-iqe-explainable-image-quality-evaluation","slug":"x-iqe-explainable-image-quality-evaluation","title":"X-IQE: eXplainable Image Quality Evaluation for Text-to-Image Generation with Visual Large Language Models","date":"2023-05-18","arxiv_id":"2305.10843","repositories_listed":1,"syntology":null},{"url":"/paper/controllable-mind-visual-diffusion-model","slug":"controllable-mind-visual-diffusion-model","title":"Controllable Mind Visual Diffusion Model","date":"2023-05-17","arxiv_id":"2305.10135","repositories_listed":1,"syntology":null},{"url":"/paper/fastcomposer-tuning-free-multi-subject-image","slug":"fastcomposer-tuning-free-multi-subject-image","title":"FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention","date":"2023-05-17","arxiv_id":"2305.10431","repositories_listed":1,"syntology":{"n":17,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":7,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/fastcomposer-tuning-free-multi-subject-image#ran","syntology_url":"https://syntology.ai/paper/2305.10431","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.10431"}},"official":{"repos":["mit-han-lab/fastcomposer"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/pyramid-diffusion-models-for-low-light-image","slug":"pyramid-diffusion-models-for-low-light-image","title":"Pyramid Diffusion Models For Low-light Image Enhancement","date":"2023-05-17","arxiv_id":"2305.10028","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":4,"n_instrument":3,"n_unverified":1,"n_honours":2,"n_violates":2,"n_no_contract":0,"n_pointer_only":8,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 2 honoured, 2 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/pyramid-diffusion-models-for-low-light-image#ran","syntology_url":"https://syntology.ai/paper/2305.10028","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.10028"}},"official":{"repos":["limuloo/pydiff"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/what-you-see-is-what-you-read-improving-text-1","slug":"what-you-see-is-what-you-read-improving-text-1","title":"What You See is What You Read? Improving Text-Image Alignment Evaluation","date":"2023-05-17","arxiv_id":"2305.10400","repositories_listed":1,"syntology":null},{"url":"/paper/a-conditional-denoising-diffusion","slug":"a-conditional-denoising-diffusion","title":"A Conditional Denoising Diffusion Probabilistic Model for Radio Interferometric Image Reconstruction","date":"2023-05-16","arxiv_id":"2305.09121","repositories_listed":1,"syntology":null},{"url":"/paper/towards-pragmatic-semantic-image-synthesis","slug":"towards-pragmatic-semantic-image-synthesis","title":"Towards Pragmatic Semantic Image Synthesis for Urban Scenes","date":"2023-05-16","arxiv_id":"2305.09726","repositories_listed":1,"syntology":null},{"url":"/paper/wavelet-based-unsupervised-label-to-image-1","slug":"wavelet-based-unsupervised-label-to-image-1","title":"Wavelet-based Unsupervised Label-to-Image Translation","date":"2023-05-16","arxiv_id":"2305.09647","repositories_listed":1,"syntology":null},{"url":"/paper/better-speech-synthesis-through-scaling","slug":"better-speech-synthesis-through-scaling","title":"Better speech synthesis through scaling","date":"2023-05-12","arxiv_id":"2305.07243","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/better-speech-synthesis-through-scaling#ran","syntology_url":"https://syntology.ai/paper/2305.07243","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.07243"}},"official":{"repos":["neonbjb/tortoise-tts"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/sparsegnv-generating-novel-views-of-indoor","slug":"sparsegnv-generating-novel-views-of-indoor","title":"SparseGNV: Generating Novel Views of Indoor Scenes with Sparse Input Views","date":"2023-05-11","arxiv_id":"2305.07024","repositories_listed":1,"syntology":null},{"url":"/paper/weditgan-few-shot-image-generation-via-latent","slug":"weditgan-few-shot-image-generation-via-latent","title":"WeditGAN: Few-Shot Image Generation via Latent Space Relocation","date":"2023-05-11","arxiv_id":"2305.06671","repositories_listed":1,"syntology":null},{"url":"/paper/echo-from-noise-synthetic-ultrasound-image","slug":"echo-from-noise-synthetic-ultrasound-image","title":"Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation","date":"2023-05-09","arxiv_id":"2305.05424","repositories_listed":1,"syntology":null},{"url":"/paper/multi-granularity-denoising-and-bidirectional","slug":"multi-granularity-denoising-and-bidirectional","title":"Multi-Granularity Denoising and Bidirectional Alignment for Weakly Supervised Semantic Segmentation","date":"2023-05-09","arxiv_id":"2305.05154","repositories_listed":1,"syntology":null},{"url":"/paper/sur-adapter-enhancing-text-to-image-pre","slug":"sur-adapter-enhancing-text-to-image-pre","title":"SUR-adapter: Enhancing Text-to-Image Pre-trained Diffusion Models with Large Language Models","date":"2023-05-09","arxiv_id":"2305.05189","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/sur-adapter-enhancing-text-to-image-pre#ran","syntology_url":"https://syntology.ai/paper/2305.05189","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.05189"}},"official":{"repos":["Qrange-group/SUR-adapter"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/robust-image-ordinal-regression-with","slug":"robust-image-ordinal-regression-with","title":"Robust Image Ordinal Regression with Controllable Image Generation","date":"2023-05-07","arxiv_id":"2305.04213","repositories_listed":1,"syntology":null},{"url":"/paper/text-to-image-diffusion-models-can-be-easily","slug":"text-to-image-diffusion-models-can-be-easily","title":"Text-to-Image Diffusion Models can be Easily Backdoored through Multimodal Data Poisoning","date":"2023-05-07","arxiv_id":"2305.04175","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/text-to-image-diffusion-models-can-be-easily#ran","syntology_url":"https://syntology.ai/paper/2305.04175","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.04175"}},"official":{"repos":["sf-zhai/badt2i"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/improved-techniques-for-maximum-likelihood","slug":"improved-techniques-for-maximum-likelihood","title":"Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEs","date":"2023-05-06","arxiv_id":"2305.03935","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 1 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/improved-techniques-for-maximum-likelihood#ran","syntology_url":"https://syntology.ai/paper/2305.03935","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.03935"}},"official":{"repos":["thu-ml/i-dode"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/2d-medical-image-synthesis-using-transformer","slug":"2d-medical-image-synthesis-using-transformer","title":"2D medical image synthesis using transformer-based denoising diffusion probabilistic model","date":"2023-05-05","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/breast-cancer-immunohistochemical-image","slug":"breast-cancer-immunohistochemical-image","title":"Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review","date":"2023-05-05","arxiv_id":"2305.03546","repositories_listed":1,"syntology":null},{"url":"/paper/data-curation-for-image-captioning-with-text","slug":"data-curation-for-image-captioning-with-text","title":"The Role of Data Curation in Image Captioning","date":"2023-05-05","arxiv_id":"2305.03610","repositories_listed":1,"syntology":null},{"url":"/paper/disenbooth-disentangled-parameter-efficient","slug":"disenbooth-disentangled-parameter-efficient","title":"DisenBooth: Identity-Preserving Disentangled Tuning for Subject-Driven Text-to-Image Generation","date":"2023-05-05","arxiv_id":"2305.03374","repositories_listed":1,"syntology":null},{"url":"/paper/guided-image-synthesis-via-initial-image","slug":"guided-image-synthesis-via-initial-image","title":"Guided Image Synthesis via Initial Image Editing in Diffusion Model","date":"2023-05-05","arxiv_id":"2305.03382","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/guided-image-synthesis-via-initial-image#ran","syntology_url":"https://syntology.ai/paper/2305.03382","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.03382"}},"official":{"repos":["UT-Mao/Initial-Noise-Editing"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/catch-missing-details-image-reconstruction","slug":"catch-missing-details-image-reconstruction","title":"Catch Missing Details: Image Reconstruction with Frequency Augmented Variational Autoencoder","date":"2023-05-04","arxiv_id":"2305.02541","repositories_listed":1,"syntology":{"n":15,"n_ran":13,"n_constructed":0,"n_ran_checked":11,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":3,"n_no_contract":8,"n_pointer_only":3,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 3 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/catch-missing-details-image-reconstruction#ran","syntology_url":"https://syntology.ai/paper/2305.02541","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.02541"}},"official":{"repos":["oppo-us-research/FA-VAE"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/diffusion-explainer-visual-explanation-for","slug":"diffusion-explainer-visual-explanation-for","title":"Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusion","date":"2023-05-04","arxiv_id":"2305.03509","repositories_listed":1,"syntology":null},{"url":"/paper/personalize-segment-anything-model-with-one","slug":"personalize-segment-anything-model-with-one","title":"Personalize Segment Anything Model with One Shot","date":"2023-05-04","arxiv_id":"2305.03048","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-aware-generation-of-multi-view","slug":"semantic-aware-generation-of-multi-view","title":"Semantic-aware Generation of Multi-view Portrait Drawings","date":"2023-05-04","arxiv_id":"2305.02618","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":9,"n_ran_checked":10,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":13,"phrase":"11 ran (of which 9 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/semantic-aware-generation-of-multi-view#ran","syntology_url":"https://syntology.ai/paper/2305.02618","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.02618"}},"official":{"repos":["aiart-hdu/sage"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":9,"n_ran_no_instrument_failure":10,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/multimodal-procedural-planning-via-dual-text","slug":"multimodal-procedural-planning-via-dual-text","title":"Multimodal Procedural Planning via Dual Text-Image Prompting","date":"2023-05-02","arxiv_id":"2305.01795","repositories_listed":1,"syntology":null},{"url":"/paper/pick-a-pic-an-open-dataset-of-user","slug":"pick-a-pic-an-open-dataset-of-user","title":"Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation","date":"2023-05-02","arxiv_id":"2305.01569","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pick-a-pic-an-open-dataset-of-user#ran","syntology_url":"https://syntology.ai/paper/2305.01569","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.01569"}},"official":{"repos":["yuvalkirstain/pickscore"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/self-similarity-based-super-resolution-of","slug":"self-similarity-based-super-resolution-of","title":"Self-similarity-based super-resolution of photoacoustic angiography from hand-drawn doodles","date":"2023-05-02","arxiv_id":"2305.01165","repositories_listed":1,"syntology":null},{"url":"/paper/it-is-all-about-where-you-start-text-to-image","slug":"it-is-all-about-where-you-start-text-to-image","title":"Generating images of rare concepts using pre-trained diffusion models","date":"2023-04-27","arxiv_id":"2304.14530","repositories_listed":1,"syntology":null},{"url":"/paper/diffuseexpand-expanding-dataset-for-2d","slug":"diffuseexpand-expanding-dataset-for-2d","title":"DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion models","date":"2023-04-26","arxiv_id":"2304.13416","repositories_listed":1,"syntology":null},{"url":"/paper/latent-diffusion-models-for-generative","slug":"latent-diffusion-models-for-generative","title":"Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification","date":"2023-04-25","arxiv_id":"2304.12891","repositories_listed":1,"syntology":{"n":23,"n_ran":20,"n_constructed":0,"n_ran_checked":20,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":20,"n_pointer_only":0,"phrase":"20 ran (of which 0 constructed an object rather than computing a result; 20 with no instrument failure: 0 honoured, 0 violated, 20 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/latent-diffusion-models-for-generative#ran","syntology_url":"https://syntology.ai/paper/2304.12891","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.12891"}},"official":{"repos":["meteoswiss/ldcast"],"state":"official (archive's flag): 20 ran","n_ran":20,"n_constructed":0,"n_ran_no_instrument_failure":20,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/persistently-trained-diffusion-assisted","slug":"persistently-trained-diffusion-assisted","title":"Persistently Trained, Diffusion-assisted Energy-based Models","date":"2023-04-21","arxiv_id":"2304.10707","repositories_listed":1,"syntology":null},{"url":"/paper/look-atme-the-discriminator-mean-entropy","slug":"look-atme-the-discriminator-mean-entropy","title":"Look ATME: The Discriminator Mean Entropy Needs Attention","date":"2023-04-18","arxiv_id":"2304.09024","repositories_listed":1,"syntology":null},{"url":"/paper/upgpt-universal-diffusion-model-for-person","slug":"upgpt-universal-diffusion-model-for-person","title":"UPGPT: Universal Diffusion Model for Person Image Generation, Editing and Pose Transfer","date":"2023-04-18","arxiv_id":"2304.08870","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-incompatible-knowledge-transfer-in","slug":"exploring-incompatible-knowledge-transfer-in","title":"Exploring Incompatible Knowledge Transfer in Few-shot Image Generation","date":"2023-04-15","arxiv_id":"2304.07574","repositories_listed":1,"syntology":{"n":25,"n_ran":20,"n_constructed":0,"n_ran_checked":15,"n_instrument":5,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":14,"n_pointer_only":4,"phrase":"20 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 1 honoured, 0 violated, 14 with no contract checked; 5 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/exploring-incompatible-knowledge-transfer-in#ran","syntology_url":"https://syntology.ai/paper/2304.07574","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.07574"}},"official":null}},{"url":"/paper/non-proportional-parametrizations-for-stable","slug":"non-proportional-parametrizations-for-stable","title":"Magnitude Invariant Parametrizations Improve Hypernetwork Learning","date":"2023-04-15","arxiv_id":"2304.07645","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/non-proportional-parametrizations-for-stable#ran","syntology_url":"https://syntology.ai/paper/2304.07645","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.07645"}},"official":{"repos":["jjgo/hyperlight"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/autosplice-a-text-prompt-manipulated-image","slug":"autosplice-a-text-prompt-manipulated-image","title":"AutoSplice: A Text-prompt Manipulated Image Dataset for Media Forensics","date":"2023-04-14","arxiv_id":"2304.06870","repositories_listed":1,"syntology":null},{"url":"/paper/expressive-text-to-image-generation-with-rich","slug":"expressive-text-to-image-generation-with-rich","title":"Expressive Text-to-Image Generation with Rich Text","date":"2023-04-13","arxiv_id":"2304.06720","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/expressive-text-to-image-generation-with-rich#ran","syntology_url":"https://syntology.ai/paper/2304.06720","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.06720"}},"official":{"repos":["songweige/rich-text-to-image"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/single-stage-diffusion-nerf-a-unified","slug":"single-stage-diffusion-nerf-a-unified","title":"Single-Stage Diffusion NeRF: A Unified Approach to 3D Generation and Reconstruction","date":"2023-04-13","arxiv_id":"2304.06714","repositories_listed":1,"syntology":null},{"url":"/paper/noisytwins-class-consistent-and-diverse-image","slug":"noisytwins-class-consistent-and-diverse-image","title":"NoisyTwins: Class-Consistent and Diverse Image Generation through StyleGANs","date":"2023-04-12","arxiv_id":"2304.05866","repositories_listed":1,"syntology":null},{"url":"/paper/controllable-textual-inversion-for","slug":"controllable-textual-inversion-for","title":"Controllable Textual Inversion for Personalized Text-to-Image Generation","date":"2023-04-11","arxiv_id":"2304.05265","repositories_listed":1,"syntology":null},{"url":"/paper/diffusion-models-for-constrained-domains","slug":"diffusion-models-for-constrained-domains","title":"Diffusion Models for Constrained Domains","date":"2023-04-11","arxiv_id":"2304.05364","repositories_listed":1,"syntology":null},{"url":"/paper/diffusion-recommender-model","slug":"diffusion-recommender-model","title":"Diffusion Recommender Model","date":"2023-04-11","arxiv_id":"2304.04971","repositories_listed":1,"syntology":null},{"url":"/paper/hrs-bench-holistic-reliable-and-scalable","slug":"hrs-bench-holistic-reliable-and-scalable","title":"HRS-Bench: Holistic, Reliable and Scalable Benchmark for Text-to-Image Models","date":"2023-04-11","arxiv_id":"2304.05390","repositories_listed":1,"syntology":null},{"url":"/paper/mask-conditioned-latent-diffusion-for","slug":"mask-conditioned-latent-diffusion-for","title":"Mask-conditioned latent diffusion for generating gastrointestinal polyp images","date":"2023-04-11","arxiv_id":"2304.05233","repositories_listed":1,"syntology":null},{"url":"/paper/neat-neural-artistic-tracing-for-beautiful","slug":"neat-neural-artistic-tracing-for-beautiful","title":"NeAT: Neural Artistic Tracing for Beautiful Style Transfer","date":"2023-04-11","arxiv_id":"2304.05139","repositories_listed":1,"syntology":null},{"url":"/paper/a-cheaper-and-better-diffusion-language-model","slug":"a-cheaper-and-better-diffusion-language-model","title":"A Cheaper and Better Diffusion Language Model with Soft-Masked Noise","date":"2023-04-10","arxiv_id":"2304.04746","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/a-cheaper-and-better-diffusion-language-model#ran","syntology_url":"https://syntology.ai/paper/2304.04746","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.04746"}},"official":{"repos":["amazon-science/masked-diffusion-lm"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/binary-latent-diffusion","slug":"binary-latent-diffusion","title":"Binary Latent Diffusion","date":"2023-04-10","arxiv_id":"2304.04820","repositories_listed":1,"syntology":null},{"url":"/paper/reflected-diffusion-models","slug":"reflected-diffusion-models","title":"Reflected Diffusion Models","date":"2023-04-10","arxiv_id":"2304.04740","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/reflected-diffusion-models#ran","syntology_url":"https://syntology.ai/paper/2304.04740","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.04740"}},"official":{"repos":["louaaron/Reflected-Diffusion"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/slideflow-deep-learning-for-digital","slug":"slideflow-deep-learning-for-digital","title":"Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualization","date":"2023-04-09","arxiv_id":"2304.04142","repositories_listed":1,"syntology":null},{"url":"/paper/deep-generative-modeling-with-backward","slug":"deep-generative-modeling-with-backward","title":"Deep Generative Modeling with Backward Stochastic Differential Equations","date":"2023-04-08","arxiv_id":"2304.04049","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-multimodal-sampling-via-tempered","slug":"efficient-multimodal-sampling-via-tempered","title":"Efficient Multimodal Sampling via Tempered Distribution Flow","date":"2023-04-08","arxiv_id":"2304.03933","repositories_listed":1,"syntology":null},{"url":"/paper/harnessing-the-spatial-temporal-attention-of","slug":"harnessing-the-spatial-temporal-attention-of","title":"Harnessing the Spatial-Temporal Attention of Diffusion Models for High-Fidelity Text-to-Image Synthesis","date":"2023-04-07","arxiv_id":"2304.03869","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/harnessing-the-spatial-temporal-attention-of#ran","syntology_url":"https://syntology.ai/paper/2304.03869","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.03869"}},"official":{"repos":["ucsb-nlp-chang/diffusion-spacetime-attn"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/uncurated-image-text-datasets-shedding-light","slug":"uncurated-image-text-datasets-shedding-light","title":"Uncurated Image-Text Datasets: Shedding Light on Demographic Bias","date":"2023-04-06","arxiv_id":"2304.02828","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/uncurated-image-text-datasets-shedding-light#ran","syntology_url":"https://syntology.ai/paper/2304.02828","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.02828"}},"official":{"repos":["noagarcia/phase"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/zero-shot-generative-model-adaptation-via","slug":"zero-shot-generative-model-adaptation-via","title":"Zero-shot Generative Model Adaptation via Image-specific Prompt Learning","date":"2023-04-06","arxiv_id":"2304.03119","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":8,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 1 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/zero-shot-generative-model-adaptation-via#ran","syntology_url":"https://syntology.ai/paper/2304.03119","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.03119"}},"official":{"repos":["picsart-ai-research/ipl-zero-shot-generative-model-adaptation"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/cross-modal-tumor-segmentation-using","slug":"cross-modal-tumor-segmentation-using","title":"Cross-modal tumor segmentation using generative blending augmentation and self training","date":"2023-04-04","arxiv_id":"2304.01705","repositories_listed":1,"syntology":null},{"url":"/paper/cross-modulated-few-shot-image-generation-for","slug":"cross-modulated-few-shot-image-generation-for","title":"Cross-modulated Few-shot Image Generation for Colorectal Tissue Classification","date":"2023-04-04","arxiv_id":"2304.01992","repositories_listed":1,"syntology":null},{"url":"/paper/egc-image-generation-and-classification-via-a","slug":"egc-image-generation-and-classification-via-a","title":"EGC: Image Generation and Classification via a Diffusion Energy-Based Model","date":"2023-04-04","arxiv_id":"2304.02012","repositories_listed":1,"syntology":{"n":8,"n_ran":4,"n_constructed":3,"n_ran_checked":4,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/egc-image-generation-and-classification-via-a#ran","syntology_url":"https://syntology.ai/paper/2304.02012","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.02012"}},"official":{"repos":["guoqiushan/egc"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":3,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/generative-multiplane-neural-radiance-for-3d","slug":"generative-multiplane-neural-radiance-for-3d","title":"Generative Multiplane Neural Radiance for 3D-Aware Image Generation","date":"2023-04-03","arxiv_id":"2304.01172","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/generative-multiplane-neural-radiance-for-3d#ran","syntology_url":"https://syntology.ai/paper/2304.01172","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.01172"}},"official":{"repos":["virobo-15/gmnr"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/prefgen-preference-guided-image-generation","slug":"prefgen-preference-guided-image-generation","title":"PrefGen: Preference Guided Image Generation with Relative Attributes","date":"2023-04-01","arxiv_id":"2304.00185","repositories_listed":1,"syntology":null},{"url":"/paper/pay-attention-accuracy-versus","slug":"pay-attention-accuracy-versus","title":"Trade-offs in Fine-tuned Diffusion Models Between Accuracy and Interpretability","date":"2023-03-31","arxiv_id":"2303.17908","repositories_listed":1,"syntology":null},{"url":"/paper/forget-me-not-learning-to-forget-in-text-to","slug":"forget-me-not-learning-to-forget-in-text-to","title":"Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion Models","date":"2023-03-30","arxiv_id":"2303.17591","repositories_listed":1,"syntology":null},{"url":"/paper/masked-and-adaptive-transformer-for-exemplar","slug":"masked-and-adaptive-transformer-for-exemplar","title":"Masked and Adaptive Transformer for Exemplar Based Image Translation","date":"2023-03-30","arxiv_id":"2303.17123","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":7,"n_ran_checked":8,"n_instrument":2,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"10 ran (of which 7 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/masked-and-adaptive-transformer-for-exemplar#ran","syntology_url":"https://syntology.ai/paper/2303.17123","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.17123"}},"official":{"repos":["aiart-hdu/matebit"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":7,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/sc-vae-sparse-coding-based-variational","slug":"sc-vae-sparse-coding-based-variational","title":"SC-VAE: Sparse Coding-based Variational Autoencoder with Learned ISTA","date":"2023-03-29","arxiv_id":"2303.16666","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/sc-vae-sparse-coding-based-variational#ran","syntology_url":"https://syntology.ai/paper/2303.16666","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.16666"}},"official":{"repos":["sotiraslab/SC-VAE"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/wordstylist-styled-verbatim-handwritten-text","slug":"wordstylist-styled-verbatim-handwritten-text","title":"WordStylist: Styled Verbatim Handwritten Text Generation with Latent Diffusion Models","date":"2023-03-29","arxiv_id":"2303.16576","repositories_listed":1,"syntology":null},{"url":"/paper/hyperbolic-geometry-in-computer-vision-a","slug":"hyperbolic-geometry-in-computer-vision-a","title":"Fully Hyperbolic Convolutional Neural Networks for Computer Vision","date":"2023-03-28","arxiv_id":"2303.15919","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/hyperbolic-geometry-in-computer-vision-a#ran","syntology_url":"https://syntology.ai/paper/2303.15919","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.15919"}},"official":{"repos":["kschwethelm/hyperboliccv"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/synthrad2023-grand-challenge-dataset","slug":"synthrad2023-grand-challenge-dataset","title":"SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapy","date":"2023-03-28","arxiv_id":"2303.16320","repositories_listed":1,"syntology":null},{"url":"/paper/anti-dreambooth-protecting-users-from","slug":"anti-dreambooth-protecting-users-from","title":"Anti-DreamBooth: Protecting users from personalized text-to-image synthesis","date":"2023-03-27","arxiv_id":"2303.15433","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/anti-dreambooth-protecting-users-from#ran","syntology_url":"https://syntology.ai/paper/2303.15433","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.15433"}},"official":{"repos":["vinairesearch/anti-dreambooth"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/diffusion-models-for-memory-efficient","slug":"diffusion-models-for-memory-efficient","title":"Memory-Efficient 3D Denoising Diffusion Models for Medical Image Processing","date":"2023-03-27","arxiv_id":"2303.15288","repositories_listed":1,"syntology":null},{"url":"/paper/mask-and-restore-blind-backdoor-defense-at","slug":"mask-and-restore-blind-backdoor-defense-at","title":"Mask and Restore: Blind Backdoor Defense at Test Time with Masked Autoencoder","date":"2023-03-27","arxiv_id":"2303.15564","repositories_listed":1,"syntology":{"n":15,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":7,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":3,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/mask-and-restore-blind-backdoor-defense-at#ran","syntology_url":"https://syntology.ai/paper/2303.15564","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.15564"}},"official":{"repos":["tsun/bdmae"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/relational-inductive-biases-for-object","slug":"relational-inductive-biases-for-object","title":"Object-Centric Relational Representations for Image Generation","date":"2023-03-26","arxiv_id":"2303.14681","repositories_listed":1,"syntology":null},{"url":"/paper/freestyle-layout-to-image-synthesis","slug":"freestyle-layout-to-image-synthesis","title":"Freestyle Layout-to-Image Synthesis","date":"2023-03-25","arxiv_id":"2303.14412","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/freestyle-layout-to-image-synthesis#ran","syntology_url":"https://syntology.ai/paper/2303.14412","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.14412"}},"official":{"repos":["essunny310/freestylenet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/indonesian-text-to-image-synthesis-with","slug":"indonesian-text-to-image-synthesis-with","title":"Indonesian Text-to-Image Synthesis with Sentence-BERT and FastGAN","date":"2023-03-25","arxiv_id":"2303.14517","repositories_listed":1,"syntology":null},{"url":"/paper/masked-diffusion-transformer-is-a-strong","slug":"masked-diffusion-transformer-is-a-strong","title":"MDTv2: Masked Diffusion Transformer is a Strong Image Synthesizer","date":"2023-03-25","arxiv_id":"2303.14389","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":2,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 2 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/masked-diffusion-transformer-is-a-strong#ran","syntology_url":"https://syntology.ai/paper/2303.14389","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.14389"}},"official":{"repos":["sail-sg/mdt"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":2,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/cola-diff-conditional-latent-diffusion-model","slug":"cola-diff-conditional-latent-diffusion-model","title":"CoLa-Diff: Conditional Latent Diffusion Model for Multi-Modal MRI Synthesis","date":"2023-03-24","arxiv_id":"2303.14081","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-scale-invariant-generator-with","slug":"efficient-scale-invariant-generator-with","title":"Efficient Scale-Invariant Generator with Column-Row Entangled Pixel Synthesis","date":"2023-03-24","arxiv_id":"2303.14157","repositories_listed":1,"syntology":null},{"url":"/paper/urbangiraffe-representing-urban-scenes-as","slug":"urbangiraffe-representing-urban-scenes-as","title":"UrbanGIRAFFE: Representing Urban Scenes as Compositional Generative Neural Feature Fields","date":"2023-03-24","arxiv_id":"2303.14167","repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-diffusion-latent-optimization","slug":"end-to-end-diffusion-latent-optimization","title":"End-to-End Diffusion Latent Optimization Improves Classifier Guidance","date":"2023-03-23","arxiv_id":"2303.13703","repositories_listed":1,"syntology":null},{"url":"/paper/medical-diffusion-on-a-budget-textual","slug":"medical-diffusion-on-a-budget-textual","title":"Medical diffusion on a budget: Textual Inversion for medical image generation","date":"2023-03-23","arxiv_id":"2303.13430","repositories_listed":1,"syntology":null},{"url":"/paper/panohead-geometry-aware-3d-full-head","slug":"panohead-geometry-aware-3d-full-head","title":"PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360$^{\\circ}$","date":"2023-03-23","arxiv_id":"2303.13071","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/panohead-geometry-aware-3d-full-head#ran","syntology_url":"https://syntology.ai/paper/2303.13071","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.13071"}},"official":null}},{"url":"/paper/set-the-scene-global-local-training-for","slug":"set-the-scene-global-local-training-for","title":"Set-the-Scene: Global-Local Training for Generating Controllable NeRF Scenes","date":"2023-03-23","arxiv_id":"2303.13450","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/set-the-scene-global-local-training-for#ran","syntology_url":"https://syntology.ai/paper/2303.13450","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.13450"}},"official":{"repos":["DanaCohen95/Set-the-Scene"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/text2video-zero-text-to-image-diffusion","slug":"text2video-zero-text-to-image-diffusion","title":"Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators","date":"2023-03-23","arxiv_id":"2303.13439","repositories_listed":1,"syntology":null},{"url":"/paper/feature-conditioned-cascaded-video-diffusion","slug":"feature-conditioned-cascaded-video-diffusion","title":"Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis","date":"2023-03-22","arxiv_id":"2303.12644","repositories_listed":1,"syntology":null},{"url":"/paper/nerf-gan-distillation-for-efficient-3d-aware","slug":"nerf-gan-distillation-for-efficient-3d-aware","title":"NeRF-GAN Distillation for Efficient 3D-Aware Generation with Convolutions","date":"2023-03-22","arxiv_id":"2303.12865","repositories_listed":1,"syntology":null},{"url":"/paper/vecfontsdf-learning-to-reconstruct-and","slug":"vecfontsdf-learning-to-reconstruct-and","title":"VecFontSDF: Learning to Reconstruct and Synthesize High-quality Vector Fonts via Signed Distance Functions","date":"2023-03-22","arxiv_id":"2303.12675","repositories_listed":1,"syntology":null},{"url":"/paper/diffumask-synthesizing-images-with-pixel","slug":"diffumask-synthesizing-images-with-pixel","title":"DiffuMask: Synthesizing Images with Pixel-level Annotations for Semantic Segmentation Using Diffusion Models","date":"2023-03-21","arxiv_id":"2303.11681","repositories_listed":1,"syntology":null},{"url":"/paper/magvlt-masked-generative-vision-and-language","slug":"magvlt-masked-generative-vision-and-language","title":"MAGVLT: Masked Generative Vision-and-Language Transformer","date":"2023-03-21","arxiv_id":"2303.12208","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":5,"n_ran_checked":7,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 5 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/magvlt-masked-generative-vision-and-language#ran","syntology_url":"https://syntology.ai/paper/2303.12208","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.12208"}},"official":{"repos":["kakaobrain/magvlt"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":5,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/tifa-accurate-and-interpretable-text-to-image","slug":"tifa-accurate-and-interpretable-text-to-image","title":"TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering","date":"2023-03-21","arxiv_id":"2303.11897","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/tifa-accurate-and-interpretable-text-to-image#ran","syntology_url":"https://syntology.ai/paper/2303.11897","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.11897"}},"official":{"repos":["Yushi-Hu/tifa"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/localizing-object-level-shape-variations-with","slug":"localizing-object-level-shape-variations-with","title":"Localizing Object-level Shape Variations with Text-to-Image Diffusion Models","date":"2023-03-20","arxiv_id":"2303.11306","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/localizing-object-level-shape-variations-with#ran","syntology_url":"https://syntology.ai/paper/2303.11306","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.11306"}},"official":null}},{"url":"/paper/polynomial-implicit-neural-representations","slug":"polynomial-implicit-neural-representations","title":"Polynomial Implicit Neural Representations For Large Diverse Datasets","date":"2023-03-20","arxiv_id":"2303.11424","repositories_listed":1,"syntology":null},{"url":"/paper/svdiff-compact-parameter-space-for-diffusion","slug":"svdiff-compact-parameter-space-for-diffusion","title":"SVDiff: Compact Parameter Space for Diffusion Fine-Tuning","date":"2023-03-20","arxiv_id":"2303.11305","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/svdiff-compact-parameter-space-for-diffusion#ran","syntology_url":"https://syntology.ai/paper/2303.11305","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.11305"}},"official":null}},{"url":"/paper/denoising-diffusion-autoencoders-are-unified","slug":"denoising-diffusion-autoencoders-are-unified","title":"Denoising Diffusion Autoencoders are Unified Self-supervised Learners","date":"2023-03-17","arxiv_id":"2303.09769","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":1,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/denoising-diffusion-autoencoders-are-unified#ran","syntology_url":"https://syntology.ai/paper/2303.09769","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.09769"}},"official":{"repos":["futurexiang/ddae"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/gluegen-plug-and-play-multi-modal-encoders","slug":"gluegen-plug-and-play-multi-modal-encoders","title":"GlueGen: Plug and Play Multi-modal Encoders for X-to-image Generation","date":"2023-03-17","arxiv_id":"2303.10056","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/gluegen-plug-and-play-multi-modal-encoders#ran","syntology_url":"https://syntology.ai/paper/2303.10056","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.10056"}},"official":{"repos":["salesforce/gluegen"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/diffir-efficient-diffusion-model-for-image","slug":"diffir-efficient-diffusion-model-for-image","title":"DiffIR: Efficient Diffusion Model for Image Restoration","date":"2023-03-16","arxiv_id":"2303.09472","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/diffir-efficient-diffusion-model-for-image#ran","syntology_url":"https://syntology.ai/paper/2303.09472","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.09472"}},"official":{"repos":["zj-binxia/diffir"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/image-classifiers-leak-sensitive-attributes","slug":"image-classifiers-leak-sensitive-attributes","title":"Class Attribute Inference Attacks: Inferring Sensitive Class Information by Diffusion-Based Attribute Manipulations","date":"2023-03-16","arxiv_id":"2303.09289","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/image-classifiers-leak-sensitive-attributes#ran","syntology_url":"https://syntology.ai/paper/2303.09289","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.09289"}},"official":{"repos":["lukasstruppek/class_attribute_inference_attacks"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/p-extended-textual-conditioning-in-text-to","slug":"p-extended-textual-conditioning-in-text-to","title":"P+: Extended Textual Conditioning in Text-to-Image Generation","date":"2023-03-16","arxiv_id":"2303.09522","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/p-extended-textual-conditioning-in-text-to#ran","syntology_url":"https://syntology.ai/paper/2303.09522","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.09522"}},"official":null}},{"url":"/paper/spectralclip-preventing-artifacts-in-text","slug":"spectralclip-preventing-artifacts-in-text","title":"SpectralCLIP: Preventing Artifacts in Text-Guided Style Transfer from a Spectral Perspective","date":"2023-03-16","arxiv_id":"2303.09270","repositories_listed":1,"syntology":null},{"url":"/paper/improving-3d-imaging-with-pre-trained","slug":"improving-3d-imaging-with-pre-trained","title":"Improving 3D Imaging with Pre-Trained Perpendicular 2D Diffusion Models","date":"2023-03-15","arxiv_id":"2303.08440","repositories_listed":1,"syntology":null}],"record_sha256":"a602c6dbeba16e1118e34e438479911945c828c6cf6ad41d9150c15bacdfd9d7","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}