{"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/30","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":30,"pages_in_order":67,"rows_per_page":100,"rows":[2901,3000],"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/29","next":"/task/image-generation/papers/31","papers":[{"url":"/paper/bridging-the-gap-between-f-gans-and-1","slug":"bridging-the-gap-between-f-gans-and-1","title":"Bridging the Gap Between f-GANs and Wasserstein GANs","date":"2020-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/fhdr-hdr-image-reconstruction-from-a-single","slug":"fhdr-hdr-image-reconstruction-from-a-single","title":"FHDR: HDR Image Reconstruction from a Single LDR Image using Feedback Network","date":"2019-12-24","arxiv_id":"1912.11463","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"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 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) · 0 unverified","sample_list":"/paper/fhdr-hdr-image-reconstruction-from-a-single#ran","syntology_url":"https://syntology.ai/paper/1912.11463","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.11463"}},"official":{"repos":["mukulkhanna/fhdr"],"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/rpgan-gans-interpretability-via-random","slug":"rpgan-gans-interpretability-via-random","title":"RPGAN: GANs Interpretability via Random Routing","date":"2019-12-23","arxiv_id":"1912.10920","repositories_listed":1,"syntology":null},{"url":"/paper/bridging-adversarial-samples-and-adversarial-1","slug":"bridging-adversarial-samples-and-adversarial-1","title":"Adversarial symmetric GANs: bridging adversarial samples and adversarial networks","date":"2019-12-20","arxiv_id":"1912.09670","repositories_listed":1,"syntology":null},{"url":"/paper/triple-generative-adversarial-networks","slug":"triple-generative-adversarial-networks","title":"Triple Generative Adversarial Networks","date":"2019-12-20","arxiv_id":"1912.09784","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"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) · 3 unverified","sample_list":"/paper/triple-generative-adversarial-networks#ran","syntology_url":"https://syntology.ai/paper/1912.09784","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.09784"}},"official":{"repos":["taufikxu/Triple-GAN"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/cpgan-full-spectrum-content-parsing","slug":"cpgan-full-spectrum-content-parsing","title":"CPGAN: Full-Spectrum Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis","date":"2019-12-18","arxiv_id":"1912.08562","repositories_listed":1,"syntology":null},{"url":"/paper/image-processing-using-multi-code-gan-prior","slug":"image-processing-using-multi-code-gan-prior","title":"Image Processing Using Multi-Code GAN Prior","date":"2019-12-15","arxiv_id":"1912.07116","repositories_listed":1,"syntology":null},{"url":"/paper/unified-generative-adversarial-networks-for","slug":"unified-generative-adversarial-networks-for","title":"Unified Generative Adversarial Networks for Controllable Image-to-Image Translation","date":"2019-12-12","arxiv_id":"1912.06112","repositories_listed":1,"syntology":null},{"url":"/paper/towards-unsupervised-learning-of-generative","slug":"towards-unsupervised-learning-of-generative","title":"Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis","date":"2019-12-11","arxiv_id":"1912.05237","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/towards-unsupervised-learning-of-generative#ran","syntology_url":"https://syntology.ai/paper/1912.05237","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.05237"}},"official":{"repos":["autonomousvision/controllable_image_synthesis"],"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/neural-voxel-renderer-learning-an-accurate","slug":"neural-voxel-renderer-learning-an-accurate","title":"Neural Voxel Renderer: Learning an Accurate and Controllable Rendering Tool","date":"2019-12-10","arxiv_id":"1912.04591","repositories_listed":1,"syntology":null},{"url":"/paper/learning-disentangled-representations-via-1","slug":"learning-disentangled-representations-via-1","title":"Learning Disentangled Representations via Mutual Information Estimation","date":"2019-12-09","arxiv_id":"1912.03915","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":1,"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/learning-disentangled-representations-via-1#ran","syntology_url":"https://syntology.ai/paper/1912.03915","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.03915"}},"official":null}},{"url":"/paper/learning-structure-appearance-joint-embedding","slug":"learning-structure-appearance-joint-embedding","title":"Neural Wireframe Renderer: Learning Wireframe to Image Translations","date":"2019-12-09","arxiv_id":"1912.03840","repositories_listed":1,"syntology":null},{"url":"/paper/connecting-vision-and-language-with-localized","slug":"connecting-vision-and-language-with-localized","title":"Connecting Vision and Language with Localized Narratives","date":"2019-12-06","arxiv_id":"1912.03098","repositories_listed":1,"syntology":null},{"url":"/paper/controlling-style-and-semantics-in-weakly","slug":"controlling-style-and-semantics-in-weakly","title":"Controlling Style and Semantics in Weakly-Supervised Image Generation","date":"2019-12-06","arxiv_id":"1912.03161","repositories_listed":1,"syntology":null},{"url":"/paper/adversarialnas-adversarial-neural","slug":"adversarialnas-adversarial-neural","title":"AdversarialNAS: Adversarial Neural Architecture Search for GANs","date":"2019-12-04","arxiv_id":"1912.02037","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 1 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/adversarialnas-adversarial-neural#ran","syntology_url":"https://syntology.ai/paper/1912.02037","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.02037"}},"official":{"repos":["chengaopro/AdversarialNAS"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/hi-cmd-hierarchical-cross-modality","slug":"hi-cmd-hierarchical-cross-modality","title":"Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification","date":"2019-12-03","arxiv_id":"1912.01230","repositories_listed":1,"syntology":null},{"url":"/paper/logan-latent-optimisation-for-generative-1","slug":"logan-latent-optimisation-for-generative-1","title":"LOGAN: Latent Optimisation for Generative Adversarial Networks","date":"2019-12-02","arxiv_id":"1912.00953","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":2,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/logan-latent-optimisation-for-generative-1#ran","syntology_url":"https://syntology.ai/paper/1912.00953","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.00953"}},"official":null}},{"url":"/paper/learn-imagine-and-create-text-to-image","slug":"learn-imagine-and-create-text-to-image","title":"Learn, Imagine and Create: Text-to-Image Generation from Prior Knowledge","date":"2019-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/twin-auxilary-classifiers-gan","slug":"twin-auxilary-classifiers-gan","title":"Twin Auxilary Classifiers GAN","date":"2019-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/sean-image-synthesis-with-semantic-region","slug":"sean-image-synthesis-with-semantic-region","title":"SEAN: Image Synthesis with Semantic Region-Adaptive Normalization","date":"2019-11-28","arxiv_id":"1911.12861","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-gan-analysis-and-improvement-1","slug":"self-supervised-gan-analysis-and-improvement-1","title":"Self-supervised GAN: Analysis and Improvement with Multi-class Minimax Game","date":"2019-11-16","arxiv_id":"1911.06997","repositories_listed":1,"syntology":null},{"url":"/paper/quality-aware-generative-adversarial-networks","slug":"quality-aware-generative-adversarial-networks","title":"Quality Aware Generative Adversarial Networks","date":"2019-11-08","arxiv_id":"1911.03149","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/quality-aware-generative-adversarial-networks#ran","syntology_url":"https://syntology.ai/paper/1911.03149","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.03149"}},"official":{"repos":["lfovia/QAGANS"],"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","unlocated"]}}},{"url":"/paper/pixel-wise-conditioning-of-generative","slug":"pixel-wise-conditioning-of-generative","title":"Pixel-wise Conditioning of Generative Adversarial Networks","date":"2019-11-02","arxiv_id":"1911.00689","repositories_listed":1,"syntology":null},{"url":"/paper/denoising-and-regularization-via-exploiting","slug":"denoising-and-regularization-via-exploiting","title":"Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators","date":"2019-10-31","arxiv_id":"1910.14634","repositories_listed":1,"syntology":null},{"url":"/paper/fair-generative-modeling-via-weak-supervision","slug":"fair-generative-modeling-via-weak-supervision","title":"Fair Generative Modeling via Weak Supervision","date":"2019-10-26","arxiv_id":"1910.12008","repositories_listed":1,"syntology":null},{"url":"/paper/study-of-deep-generative-models-for-inorganic","slug":"study-of-deep-generative-models-for-inorganic","title":"Study of Deep Generative Models for Inorganic Chemical Compositions","date":"2019-10-25","arxiv_id":"1910.11499","repositories_listed":1,"syntology":null},{"url":"/paper/bridging-the-gap-between-f-gans-and","slug":"bridging-the-gap-between-f-gans-and","title":"Bridging the Gap Between $f$-GANs and Wasserstein GANs","date":"2019-10-22","arxiv_id":"1910.09779","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/bridging-the-gap-between-f-gans-and#ran","syntology_url":"https://syntology.ai/paper/1910.09779","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.09779"}},"official":{"repos":["ermongroup/f-wgan"],"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/image-difficulty-curriculum-for-generative","slug":"image-difficulty-curriculum-for-generative","title":"Image Difficulty Curriculum for Generative Adversarial Networks (CuGAN)","date":"2019-10-20","arxiv_id":"1910.08967","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-predict-layout-to-image","slug":"learning-to-predict-layout-to-image","title":"Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis","date":"2019-10-15","arxiv_id":"1910.06809","repositories_listed":1,"syntology":null},{"url":"/paper/defending-neural-backdoors-via-generative","slug":"defending-neural-backdoors-via-generative","title":"Defending Neural Backdoors via Generative Distribution Modeling","date":"2019-10-10","arxiv_id":"1910.04749","repositories_listed":1,"syntology":null},{"url":"/paper/clothflow-a-flow-based-model-for-clothed","slug":"clothflow-a-flow-based-model-for-clothed","title":"ClothFlow: A Flow-Based Model for Clothed Person Generation","date":"2019-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/faceforensics-learning-to-detect-manipulated-1","slug":"faceforensics-learning-to-detect-manipulated-1","title":"FaceForensics++: Learning to Detect Manipulated Facial Images","date":"2019-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/vtnfp-an-image-based-virtual-try-on-network","slug":"vtnfp-an-image-based-virtual-try-on-network","title":"VTNFP: An Image-Based Virtual Try-On Network With Body and Clothing Feature Preservation","date":"2019-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/understanding-and-stabilizing-gans-training","slug":"understanding-and-stabilizing-gans-training","title":"Understanding and Stabilizing GANs' Training Dynamics with Control Theory","date":"2019-09-29","arxiv_id":"1909.13188","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/understanding-and-stabilizing-gans-training#ran","syntology_url":"https://syntology.ai/paper/1909.13188","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.13188"}},"official":null}},{"url":"/paper/rpgan-random-paths-as-a-latent-space-for-gan","slug":"rpgan-random-paths-as-a-latent-space-for-gan","title":"RPGAN: random paths as a latent space for GAN interpretability","date":"2019-09-25","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/subsampling-generative-adversarial-networks","slug":"subsampling-generative-adversarial-networks","title":"Subsampling Generative Adversarial Networks: Density Ratio Estimation in Feature Space with Softplus Loss","date":"2019-09-24","arxiv_id":"1909.10670","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/subsampling-generative-adversarial-networks#ran","syntology_url":"https://syntology.ai/paper/1909.10670","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.10670"}},"official":{"repos":["UBCDingXin/DDRE_Sampling_GANs"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/synthetic-dataset-generation-for-object-to","slug":"synthetic-dataset-generation-for-object-to","title":"Synthetic dataset generation for object-to-model deep learning in industrial applications","date":"2019-09-24","arxiv_id":"1909.10976","repositories_listed":1,"syntology":null},{"url":"/paper/an-unpaired-sketch-to-photo-translation-model","slug":"an-unpaired-sketch-to-photo-translation-model","title":"Unsupervised Sketch-to-Photo Synthesis","date":"2019-09-18","arxiv_id":"1909.08313","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":6,"n_pointer_only":3,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/an-unpaired-sketch-to-photo-translation-model#ran","syntology_url":"https://syntology.ai/paper/1909.08313","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.08313"}},"official":null}},{"url":"/paper/a-characteristic-function-approach-to-deep","slug":"a-characteristic-function-approach-to-deep","title":"A Characteristic Function Approach to Deep Implicit Generative Modeling","date":"2019-09-16","arxiv_id":"1909.07425","repositories_listed":1,"syntology":{"n":12,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/a-characteristic-function-approach-to-deep#ran","syntology_url":"https://syntology.ai/paper/1909.07425","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.07425"}},"official":{"repos":["crslab/OCFGAN"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/-vae-autoregressive-parametrization-of-the","slug":"-vae-autoregressive-parametrization-of-the","title":"$ρ$-VAE: Autoregressive parametrization of the VAE encoder","date":"2019-09-13","arxiv_id":"1909.06236","repositories_listed":1,"syntology":null},{"url":"/paper/torchgan-a-flexible-framework-for-gan","slug":"torchgan-a-flexible-framework-for-gan","title":"TorchGAN: A Flexible Framework for GAN Training and Evaluation","date":"2019-09-08","arxiv_id":"1909.03410","repositories_listed":1,"syntology":null},{"url":"/paper/boovae-a-scalable-framework-for-continual-vae","slug":"boovae-a-scalable-framework-for-continual-vae","title":"BooVAE: Boosting Approach for Continual Learning of VAE","date":"2019-08-30","arxiv_id":"1908.11853","repositories_listed":1,"syntology":null},{"url":"/paper/dual-glow-conditional-flow-based-generative","slug":"dual-glow-conditional-flow-based-generative","title":"DUAL-GLOW: Conditional Flow-Based Generative Model for Modality Transfer","date":"2019-08-21","arxiv_id":"1908.08074","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/dual-glow-conditional-flow-based-generative#ran","syntology_url":"https://syntology.ai/paper/1908.08074","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.08074"}},"official":{"repos":["haolsun/dual-glow"],"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/unpaired-image-to-speech-synthesis-with","slug":"unpaired-image-to-speech-synthesis-with","title":"Unpaired Image-to-Speech Synthesis with Multimodal Information Bottleneck","date":"2019-08-19","arxiv_id":"1908.07094","repositories_listed":1,"syntology":null},{"url":"/paper/cycle-in-cycle-generative-adversarial","slug":"cycle-in-cycle-generative-adversarial","title":"Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image Generation","date":"2019-08-02","arxiv_id":"1908.00999","repositories_listed":1,"syntology":null},{"url":"/paper/semi-parametric-object-synthesis","slug":"semi-parametric-object-synthesis","title":"Warp and Learn: Novel Views Generation for Vehicles and Other Objects","date":"2019-07-24","arxiv_id":"1907.10634","repositories_listed":1,"syntology":null},{"url":"/paper/mintnet-building-invertible-neural-networks","slug":"mintnet-building-invertible-neural-networks","title":"MintNet: Building Invertible Neural Networks with Masked Convolutions","date":"2019-07-18","arxiv_id":"1907.07945","repositories_listed":1,"syntology":null},{"url":"/paper/synthtext3d-synthesizing-scene-text-images","slug":"synthtext3d-synthesizing-scene-text-images","title":"SynthText3D: Synthesizing Scene Text Images from 3D Virtual Worlds","date":"2019-07-13","arxiv_id":"1907.06007","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-for-fine-grained-image-analysis","slug":"deep-learning-for-fine-grained-image-analysis","title":"Deep Learning for Fine-Grained Image Analysis: A Survey","date":"2019-07-06","arxiv_id":"1907.03069","repositories_listed":1,"syntology":null},{"url":"/paper/fully-automatic-computer-aided-mass-detection","slug":"fully-automatic-computer-aided-mass-detection","title":"Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and Mask R-CNN","date":"2019-06-28","arxiv_id":"1906.12118","repositories_listed":1,"syntology":null},{"url":"/paper/inspirational-adversarial-image-generation","slug":"inspirational-adversarial-image-generation","title":"Inspirational Adversarial Image Generation","date":"2019-06-17","arxiv_id":"1906.11661","repositories_listed":1,"syntology":null},{"url":"/paper/multi-objects-generation-with-amortized","slug":"multi-objects-generation-with-amortized","title":"Multi-objects Generation with Amortized Structural Regularization","date":"2019-06-10","arxiv_id":"1906.03923","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/multi-objects-generation-with-amortized#ran","syntology_url":"https://syntology.ai/paper/1906.03923","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.03923"}},"official":{"repos":["taufikxu/MOG-ASR"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/the-implicit-metropolis-hastings-algorithm","slug":"the-implicit-metropolis-hastings-algorithm","title":"The Implicit Metropolis-Hastings Algorithm","date":"2019-06-09","arxiv_id":"1906.03644","repositories_listed":1,"syntology":null},{"url":"/paper/conditional-neural-style-transfer-with-peer","slug":"conditional-neural-style-transfer-with-peer","title":"Two-Stage Peer-Regularized Feature Recombination for Arbitrary Image Style Transfer","date":"2019-06-07","arxiv_id":"1906.02913","repositories_listed":1,"syntology":null},{"url":"/paper/computer-vision-with-a-single-robust","slug":"computer-vision-with-a-single-robust","title":"Image Synthesis with a Single (Robust) Classifier","date":"2019-06-06","arxiv_id":"1906.09453","repositories_listed":1,"syntology":null},{"url":"/paper/example-guided-style-consistent-image-1","slug":"example-guided-style-consistent-image-1","title":"Example-Guided Style Consistent Image Synthesis from Semantic Labeling","date":"2019-06-04","arxiv_id":"1906.01314","repositories_listed":1,"syntology":null},{"url":"/paper/190600184","slug":"190600184","title":"ZstGAN: An Adversarial Approach for Unsupervised Zero-Shot Image-to-Image Translation","date":"2019-06-01","arxiv_id":"1906.00184","repositories_listed":1,"syntology":null},{"url":"/paper/training-generative-adversarial-networks-from","slug":"training-generative-adversarial-networks-from","title":"Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators","date":"2019-05-29","arxiv_id":"1905.12660","repositories_listed":1,"syntology":null},{"url":"/paper/generative-latent-flow-a-framework-for-non","slug":"generative-latent-flow-a-framework-for-non","title":"Generative Latent Flow","date":"2019-05-24","arxiv_id":"1905.10485","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"5 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; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/generative-latent-flow-a-framework-for-non#ran","syntology_url":"https://syntology.ai/paper/1905.10485","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.10485"}},"official":null}},{"url":"/paper/joint-learning-of-neural-networks-via","slug":"joint-learning-of-neural-networks-via","title":"Joint Learning of Neural Networks via Iterative Reweighted Least Squares","date":"2019-05-16","arxiv_id":"1905.06526","repositories_listed":1,"syntology":null},{"url":"/paper/kernel-mean-matching-for-content","slug":"kernel-mean-matching-for-content","title":"Kernel Mean Matching for Content Addressability of GANs","date":"2019-05-14","arxiv_id":"1905.05882","repositories_listed":1,"syntology":null},{"url":"/paper/ink-removal-from-histopathology-whole-slide","slug":"ink-removal-from-histopathology-whole-slide","title":"Ink removal from histopathology whole slide images by combining classification, detection and image generation models","date":"2019-05-10","arxiv_id":"1905.04385","repositories_listed":1,"syntology":null},{"url":"/paper/neuroscore-a-brain-inspired-evaluation-metric","slug":"neuroscore-a-brain-inspired-evaluation-metric","title":"Synthetic-Neuroscore: Using A Neuro-AI Interface for Evaluating Generative Adversarial Networks","date":"2019-05-10","arxiv_id":"1905.04243","repositories_listed":1,"syntology":null},{"url":"/paper/190513149","slug":"190513149","title":"The Art of Food: Meal Image Synthesis from Ingredients","date":"2019-05-09","arxiv_id":"1905.13149","repositories_listed":1,"syntology":null},{"url":"/paper/spatially-constrained-generative-adversarial","slug":"spatially-constrained-generative-adversarial","title":"Spatially Constrained GAN for Face and Fashion Synthesis","date":"2019-05-07","arxiv_id":"1905.02320","repositories_listed":1,"syntology":null},{"url":"/paper/pastegan-a-semi-parametric-method-to-generate","slug":"pastegan-a-semi-parametric-method-to-generate","title":"PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph","date":"2019-05-05","arxiv_id":"1905.01608","repositories_listed":1,"syntology":null},{"url":"/paper/learn-to-synthesize-and-synthesize-to-learn","slug":"learn-to-synthesize-and-synthesize-to-learn","title":"Learn to synthesize and synthesize to learn","date":"2019-05-01","arxiv_id":"1905.00286","repositories_listed":1,"syntology":null},{"url":"/paper/overcoming-the-disentanglement-vs","slug":"overcoming-the-disentanglement-vs","title":"Overcoming the Disentanglement vs Reconstruction Trade-off via Jacobian Supervision","date":"2019-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/probgan-towards-probabilistic-gan-with","slug":"probgan-towards-probabilistic-gan-with","title":"ProbGAN: Towards Probabilistic GAN with Theoretical Guarantees","date":"2019-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/appearance-and-pose-conditioned-human-image","slug":"appearance-and-pose-conditioned-human-image","title":"Appearance and Pose-Conditioned Human Image Generation using Deformable GANs","date":"2019-04-30","arxiv_id":"1905.00007","repositories_listed":1,"syntology":null},{"url":"/paper/diamondgan-unified-multi-modal-generative","slug":"diamondgan-unified-multi-modal-generative","title":"DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesis","date":"2019-04-29","arxiv_id":"1904.12894","repositories_listed":1,"syntology":null},{"url":"/paper/tilegan-synthesis-of-large-scale-non","slug":"tilegan-synthesis-of-large-scale-non","title":"TileGAN: Synthesis of Large-Scale Non-Homogeneous Textures","date":"2019-04-29","arxiv_id":"1904.12795","repositories_listed":1,"syntology":null},{"url":"/paper/190501976","slug":"190501976","title":"TextKD-GAN: Text Generation using KnowledgeDistillation and Generative Adversarial Networks","date":"2019-04-23","arxiv_id":"1905.01976","repositories_listed":1,"syntology":null},{"url":"/paper/deep-residual-inception-encoder-decoder","slug":"deep-residual-inception-encoder-decoder","title":"Deep residual inception encoder–decoder network for medical imaging synthesis","date":"2019-04-22","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/sliced-wasserstein-generative-models-1","slug":"sliced-wasserstein-generative-models-1","title":"Sliced Wasserstein Generative Models","date":"2019-04-10","arxiv_id":"1904.05408","repositories_listed":1,"syntology":null},{"url":"/paper/learning-monocular-depth-estimation-infusing","slug":"learning-monocular-depth-estimation-infusing","title":"Learning monocular depth estimation infusing traditional stereo knowledge","date":"2019-04-08","arxiv_id":"1904.04144","repositories_listed":1,"syntology":null},{"url":"/paper/normalized-diversification","slug":"normalized-diversification","title":"Normalized Diversification","date":"2019-04-07","arxiv_id":"1904.03608","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":2,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 2 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/normalized-diversification#ran","syntology_url":"https://syntology.ai/paper/1904.03608","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.03608"}},"official":null}},{"url":"/paper/unsupervised-person-image-generation-with","slug":"unsupervised-person-image-generation-with","title":"Unsupervised Person Image Generation with Semantic Parsing Transformation","date":"2019-04-06","arxiv_id":"1904.03379","repositories_listed":1,"syntology":null},{"url":"/paper/gan-you-do-the-gan-gan","slug":"gan-you-do-the-gan-gan","title":"GAN You Do the GAN GAN?","date":"2019-04-01","arxiv_id":"1904.00724","repositories_listed":1,"syntology":null},{"url":"/paper/coco-gan-generation-by-parts-via-conditional","slug":"coco-gan-generation-by-parts-via-conditional","title":"COCO-GAN: Generation by Parts via Conditional Coordinating","date":"2019-03-30","arxiv_id":"1904.00284","repositories_listed":1,"syntology":{"n":6,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/coco-gan-generation-by-parts-via-conditional#ran","syntology_url":"https://syntology.ai/paper/1904.00284","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.00284"}},"official":null}},{"url":"/paper/auto-embedding-generative-adversarial","slug":"auto-embedding-generative-adversarial","title":"Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis","date":"2019-03-27","arxiv_id":"1903.11250","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-unconstrained-action-unit","slug":"weakly-supervised-unconstrained-action-unit","title":"Unconstrained Facial Action Unit Detection via Latent Feature Domain","date":"2019-03-25","arxiv_id":"1903.10143","repositories_listed":1,"syntology":null},{"url":"/paper/lemotif-abstract-visual-depictions-of-your","slug":"lemotif-abstract-visual-depictions-of-your","title":"Lemotif: An Affective Visual Journal Using Deep Neural Networks","date":"2019-03-18","arxiv_id":"1903.07766","repositories_listed":1,"syntology":null},{"url":"/paper/lumipath-towards-real-time-physically-based","slug":"lumipath-towards-real-time-physically-based","title":"LumiPath -- Towards Real-time Physically-based Rendering on Embedded Devices","date":"2019-03-09","arxiv_id":"1903.03837","repositories_listed":1,"syntology":null},{"url":"/paper/out-domain-examples-for-generative-models","slug":"out-domain-examples-for-generative-models","title":"Adversarial Out-domain Examples for Generative Models","date":"2019-03-07","arxiv_id":"1903.02926","repositories_listed":1,"syntology":null},{"url":"/paper/high-fidelity-image-generation-with-fewer","slug":"high-fidelity-image-generation-with-fewer","title":"High-Fidelity Image Generation With Fewer Labels","date":"2019-03-06","arxiv_id":"1903.02271","repositories_listed":1,"syntology":null},{"url":"/paper/object-driven-text-to-image-synthesis-via","slug":"object-driven-text-to-image-synthesis-via","title":"Object-driven Text-to-Image Synthesis via Adversarial Training","date":"2019-02-27","arxiv_id":"1902.10740","repositories_listed":1,"syntology":null},{"url":"/paper/synthesizing-new-retinal-symptom-images-by","slug":"synthesizing-new-retinal-symptom-images-by","title":"Synthesizing New Retinal Symptom Images by Multiple Generative Models","date":"2019-02-11","arxiv_id":"1902.04147","repositories_listed":1,"syntology":null},{"url":"/paper/a-layer-based-sequential-framework-for-scene","slug":"a-layer-based-sequential-framework-for-scene","title":"A Layer-Based Sequential Framework for Scene Generation with GANs","date":"2019-02-02","arxiv_id":"1902.00671","repositories_listed":1,"syntology":null},{"url":"/paper/collaborative-gan-sampling","slug":"collaborative-gan-sampling","title":"Collaborative Sampling in Generative Adversarial Networks","date":"2019-02-02","arxiv_id":"1902.00813","repositories_listed":1,"syntology":null},{"url":"/paper/tf-replicator-distributed-machine-learning","slug":"tf-replicator-distributed-machine-learning","title":"TF-Replicator: Distributed Machine Learning for Researchers","date":"2019-02-01","arxiv_id":"1902.00465","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/tf-replicator-distributed-machine-learning#ran","syntology_url":"https://syntology.ai/paper/1902.00465","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.00465"}},"official":{"repos":["tensorflow/community"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/emerging-convolutions-for-generative","slug":"emerging-convolutions-for-generative","title":"Emerging Convolutions for Generative Normalizing Flows","date":"2019-01-30","arxiv_id":"1901.11137","repositories_listed":1,"syntology":null},{"url":"/paper/pa-gan-improving-gan-training-by-progressive","slug":"pa-gan-improving-gan-training-by-progressive","title":"Progressive Augmentation of GANs","date":"2019-01-29","arxiv_id":"1901.10422","repositories_listed":1,"syntology":null},{"url":"/paper/tgan-deep-tensor-generative-adversarial-nets","slug":"tgan-deep-tensor-generative-adversarial-nets","title":"TGAN: Deep Tensor Generative Adversarial Nets for Large Image Generation","date":"2019-01-28","arxiv_id":"1901.09953","repositories_listed":1,"syntology":null},{"url":"/paper/virtual-conditional-generative-adversarial","slug":"virtual-conditional-generative-adversarial","title":"Virtual Conditional Generative Adversarial Networks","date":"2019-01-25","arxiv_id":"1901.09822","repositories_listed":1,"syntology":null},{"url":"/paper/unpaired-pose-guided-human-image-generation","slug":"unpaired-pose-guided-human-image-generation","title":"Unpaired Pose Guided Human Image Generation","date":"2019-01-08","arxiv_id":"1901.02284","repositories_listed":1,"syntology":null},{"url":"/paper/generating-multiple-objects-at-spatially","slug":"generating-multiple-objects-at-spatially","title":"Generating Multiple Objects at Spatially Distinct Locations","date":"2019-01-03","arxiv_id":"1901.00686","repositories_listed":1,"syntology":null},{"url":"/paper/improving-mmd-gan-training-with-repulsive","slug":"improving-mmd-gan-training-with-repulsive","title":"Improving MMD-GAN Training with Repulsive Loss Function","date":"2018-12-24","arxiv_id":"1812.09916","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 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) · 2 unverified","sample_list":"/paper/improving-mmd-gan-training-with-repulsive#ran","syntology_url":"https://syntology.ai/paper/1812.09916","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.09916"}},"official":{"repos":["richardwth/MMD-GAN"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/generative-models-from-the-perspective-of","slug":"generative-models-from-the-perspective-of","title":"Generative Models from the perspective of Continual Learning","date":"2018-12-21","arxiv_id":"1812.09111","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/generative-models-from-the-perspective-of#ran","syntology_url":"https://syntology.ai/paper/1812.09111","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.09111"}},"official":{"repos":["TLESORT/Generative_Continual_Learning"],"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/non-adversarial-image-synthesis-with","slug":"non-adversarial-image-synthesis-with","title":"Non-Adversarial Image Synthesis with Generative Latent Nearest Neighbors","date":"2018-12-21","arxiv_id":"1812.08985","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/non-adversarial-image-synthesis-with#ran","syntology_url":"https://syntology.ai/paper/1812.08985","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.08985"}},"official":null}}],"record_sha256":"7314dd5bd2a698f79f83c21d9eeff57c6236c97e2db871d9df680aae579d9d0a","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}