{"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/disentanglement/papers/2","list_of":"/task/disentanglement","task":"Disentanglement","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":2,"pages_in_order":19,"rows_per_page":100,"rows":[101,200],"of":1854,"counts":{"archive_papers_tagged":1854,"with_a_code_link":744,"where_syntology_ran_a_sample":214,"not_listed_spam_title":0,"listed":1854,"listed_where_code_ran":214,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":180,"every_run_a_failure_of_syntologys_instrument":34,"listed_with_a_run_with_no_instrument_failure":180,"listed_every_run_a_failure_of_syntologys_instrument":34,"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/disentanglement","prev":"/task/disentanglement","next":"/task/disentanglement/papers/3","papers":[{"url":"/paper/causal-llava-causal-disentanglement-for","slug":"causal-llava-causal-disentanglement-for","title":"Causal-LLaVA: Causal Disentanglement for Mitigating Hallucination in Multimodal Large Language Models","date":"2025-05-26","arxiv_id":"2505.19474","repositories_listed":1,"syntology":{"n":19,"n_ran":8,"n_constructed":0,"n_ran_checked":4,"n_instrument":4,"n_unverified":11,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 4 where Syntology's instrument failed) · 11 unverified","sample_list":"/paper/causal-llava-causal-disentanglement-for#ran","syntology_url":"https://syntology.ai/paper/2505.19474","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.19474"}},"official":{"repos":["ignisavium/causal-llava"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":11,"ran_from_kinds":["official"]}}},{"url":"/paper/interpretability-illusions-with-sparse","slug":"interpretability-illusions-with-sparse","title":"Interpretability Illusions with Sparse Autoencoders: Evaluating Robustness of Concept Representations","date":"2025-05-21","arxiv_id":"2505.16004","repositories_listed":1,"syntology":null},{"url":"/paper/instructing-text-to-image-diffusion-models","slug":"instructing-text-to-image-diffusion-models","title":"Instructing Text-to-Image Diffusion Models via Classifier-Guided Semantic Optimization","date":"2025-05-20","arxiv_id":"2505.14254","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/instructing-text-to-image-diffusion-models#ran","syntology_url":"https://syntology.ai/paper/2505.14254","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.14254"}},"official":{"repos":["chang-yuanyuan/caso"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/tt-df-a-large-scale-diffusion-based-dataset","slug":"tt-df-a-large-scale-diffusion-based-dataset","title":"TT-DF: A Large-Scale Diffusion-Based Dataset and Benchmark for Human Body Forgery Detection","date":"2025-05-13","arxiv_id":"2505.08437","repositories_listed":1,"syntology":null},{"url":"/paper/towards-a-unified-representation-evaluation","slug":"towards-a-unified-representation-evaluation","title":"Towards a Unified Representation Evaluation Framework Beyond Downstream Tasks","date":"2025-05-09","arxiv_id":"2505.06224","repositories_listed":1,"syntology":null},{"url":"/paper/apply-hierarchical-chain-of-generation-to","slug":"apply-hierarchical-chain-of-generation-to","title":"Apply Hierarchical-Chain-of-Generation to Complex Attributes Text-to-3D Generation","date":"2025-05-07","arxiv_id":"2505.05505","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":0,"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/apply-hierarchical-chain-of-generation-to#ran","syntology_url":"https://syntology.ai/paper/2505.05505","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.05505"}},"official":{"repos":["wakals/gascol"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/dfvo-learning-darkness-free-visible-and","slug":"dfvo-learning-darkness-free-visible-and","title":"DFVO: Learning Darkness-free Visible and Infrared Image Disentanglement and Fusion All at Once","date":"2025-05-07","arxiv_id":"2505.04526","repositories_listed":1,"syntology":null},{"url":"/paper/reliable-disentanglement-multi-view-learning","slug":"reliable-disentanglement-multi-view-learning","title":"Reliable Disentanglement Multi-view Learning Against View Adversarial Attacks","date":"2025-05-07","arxiv_id":"2505.04046","repositories_listed":1,"syntology":null},{"url":"/paper/panoramic-out-of-distribution-segmentation","slug":"panoramic-out-of-distribution-segmentation","title":"Panoramic Out-of-Distribution Segmentation","date":"2025-05-06","arxiv_id":"2505.03539","repositories_listed":1,"syntology":null},{"url":"/paper/dream-disentangling-risks-to-enhance-safety","slug":"dream-disentangling-risks-to-enhance-safety","title":"DREAM: Disentangling Risks to Enhance Safety Alignment in Multimodal Large Language Models","date":"2025-04-25","arxiv_id":"2504.18053","repositories_listed":1,"syntology":null},{"url":"/paper/latent-diffusion-autoencoders-toward","slug":"latent-diffusion-autoencoders-toward","title":"Latent Diffusion Autoencoders: Toward Efficient and Meaningful Unsupervised Representation Learning in Medical Imaging","date":"2025-04-11","arxiv_id":"2504.08635","repositories_listed":1,"syntology":null},{"url":"/paper/conmo-controllable-motion-disentanglement-and","slug":"conmo-controllable-motion-disentanglement-and","title":"ConMo: Controllable Motion Disentanglement and Recomposition for Zero-Shot Motion Transfer","date":"2025-04-03","arxiv_id":"2504.02451","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":2,"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) · 1 unverified","sample_list":"/paper/conmo-controllable-motion-disentanglement-and#ran","syntology_url":"https://syntology.ai/paper/2504.02451","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2504.02451"}},"official":{"repos":["andyplus1/conmo"],"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"]}}},{"url":"/paper/efficient-model-editing-with-task-localized","slug":"efficient-model-editing-with-task-localized","title":"Efficient Model Editing with Task-Localized Sparse Fine-tuning","date":"2025-04-03","arxiv_id":"2504.02620","repositories_listed":1,"syntology":null},{"url":"/paper/eaglevision-object-level-attribute-multimodal","slug":"eaglevision-object-level-attribute-multimodal","title":"EagleVision: Object-level Attribute Multimodal LLM for Remote Sensing","date":"2025-03-30","arxiv_id":"2503.23330","repositories_listed":1,"syntology":null},{"url":"/paper/differ-disentangling-identity-features-via","slug":"differ-disentangling-identity-features-via","title":"DIFFER: Disentangling Identity Features via Semantic Cues for Clothes-Changing Person Re-ID","date":"2025-03-28","arxiv_id":"2503.22912","repositories_listed":1,"syntology":null},{"url":"/paper/slip-spoof-aware-one-class-face-anti-spoofing","slug":"slip-spoof-aware-one-class-face-anti-spoofing","title":"SLIP: Spoof-Aware One-Class Face Anti-Spoofing with Language Image Pretraining","date":"2025-03-25","arxiv_id":"2503.19982","repositories_listed":1,"syntology":null},{"url":"/paper/unik3d-universal-camera-monocular-3d","slug":"unik3d-universal-camera-monocular-3d","title":"UniK3D: Universal Camera Monocular 3D Estimation","date":"2025-03-20","arxiv_id":"2503.16591","repositories_listed":1,"syntology":null},{"url":"/paper/coe-chain-of-explanation-via-automatic-visual","slug":"coe-chain-of-explanation-via-automatic-visual","title":"CoE: Chain-of-Explanation via Automatic Visual Concept Circuit Description and Polysemanticity Quantification","date":"2025-03-19","arxiv_id":"2503.15234","repositories_listed":1,"syntology":{"n":15,"n_ran":13,"n_constructed":0,"n_ran_checked":8,"n_instrument":5,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":5,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 5 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/coe-chain-of-explanation-via-automatic-visual#ran","syntology_url":"https://syntology.ai/paper/2503.15234","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.15234"}},"official":{"repos":["YuWLong666/CoE"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/stil-semi-supervised-tabular-image-learning","slug":"stil-semi-supervised-tabular-image-learning","title":"STiL: Semi-supervised Tabular-Image Learning for Comprehensive Task-Relevant Information Exploration in Multimodal Classification","date":"2025-03-08","arxiv_id":"2503.06277","repositories_listed":1,"syntology":null},{"url":"/paper/post-hoc-concept-disentanglement-from","slug":"post-hoc-concept-disentanglement-from","title":"Post-Hoc Concept Disentanglement: From Correlated to Isolated Concept Representations","date":"2025-03-07","arxiv_id":"2503.05522","repositories_listed":1,"syntology":null},{"url":"/paper/robust-multimodal-learning-for-ophthalmic","slug":"robust-multimodal-learning-for-ophthalmic","title":"Robust Multimodal Learning for Ophthalmic Disease Grading via Disentangled Representation","date":"2025-03-07","arxiv_id":"2503.05319","repositories_listed":1,"syntology":null},{"url":"/paper/intrinsic-and-extrinsic-factor","slug":"intrinsic-and-extrinsic-factor","title":"Intrinsic and Extrinsic Factor Disentanglement for Recommendation in Various Context Scenarios","date":"2025-03-05","arxiv_id":"2503.03524","repositories_listed":1,"syntology":null},{"url":"/paper/disentangling-long-short-term-state-under","slug":"disentangling-long-short-term-state-under","title":"Disentangling Long-Short Term State Under Unknown Interventions for Online Time Series Forecasting","date":"2025-02-18","arxiv_id":"2502.12603","repositories_listed":1,"syntology":null},{"url":"/paper/are-representation-disentanglement-and","slug":"are-representation-disentanglement-and","title":"Are Representation Disentanglement and Interpretability Linked in Recommendation Models? A Critical Review and Reproducibility Study","date":"2025-01-30","arxiv_id":"2501.18805","repositories_listed":1,"syntology":null},{"url":"/paper/motion-diffusion-autoencoders-enabling","slug":"motion-diffusion-autoencoders-enabling","title":"Motion Diffusion Autoencoders: Enabling Attribute Manipulation in Human Motion Demonstrated on Karate Techniques","date":"2025-01-30","arxiv_id":"2501.18729","repositories_listed":1,"syntology":null},{"url":"/paper/reducing-aleatoric-and-epistemic-uncertainty","slug":"reducing-aleatoric-and-epistemic-uncertainty","title":"Reducing Aleatoric and Epistemic Uncertainty through Multi-modal Data Acquisition","date":"2025-01-30","arxiv_id":"2501.18268","repositories_listed":1,"syntology":null},{"url":"/paper/alphapre-amplitude-phase-disentanglement","slug":"alphapre-amplitude-phase-disentanglement","title":"AlphaPre: Amplitude-Phase Disentanglement Model for Precipitation Nowcasting","date":"2025-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/sharpening-neural-implicit-functions-with","slug":"sharpening-neural-implicit-functions-with","title":"Sharpening Neural Implicit Functions with Frequency Consolidation Priors","date":"2024-12-27","arxiv_id":"2412.19720","repositories_listed":1,"syntology":null},{"url":"/paper/improving-generalization-for-ai-synthesized","slug":"improving-generalization-for-ai-synthesized","title":"Improving Generalization for AI-Synthesized Voice Detection","date":"2024-12-26","arxiv_id":"2412.19279","repositories_listed":1,"syntology":null},{"url":"/paper/breaking-barriers-in-physical-world","slug":"breaking-barriers-in-physical-world","title":"Breaking Barriers in Physical-World Adversarial Examples: Improving Robustness and Transferability via Robust Feature","date":"2024-12-22","arxiv_id":"2412.16958","repositories_listed":1,"syntology":null},{"url":"/paper/heterogeneous-graph-collaborative-filtering-2","slug":"heterogeneous-graph-collaborative-filtering-2","title":"MixRec: Heterogeneous Graph Collaborative Filtering","date":"2024-12-18","arxiv_id":"2412.13825","repositories_listed":1,"syntology":null},{"url":"/paper/sign-idd-iconicity-disentangled-diffusion-for","slug":"sign-idd-iconicity-disentangled-diffusion-for","title":"Sign-IDD: Iconicity Disentangled Diffusion for Sign Language Production","date":"2024-12-18","arxiv_id":"2412.13609","repositories_listed":1,"syntology":null},{"url":"/paper/lifting-scheme-based-implicit-disentanglement","slug":"lifting-scheme-based-implicit-disentanglement","title":"Lifting Scheme-Based Implicit Disentanglement of Emotion-Related Facial Dynamics in the Wild","date":"2024-12-17","arxiv_id":"2412.13168","repositories_listed":1,"syntology":null},{"url":"/paper/dlf-disentangled-language-focused-multimodal","slug":"dlf-disentangled-language-focused-multimodal","title":"DLF: Disentangled-Language-Focused Multimodal Sentiment Analysis","date":"2024-12-16","arxiv_id":"2412.12225","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"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 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) · 3 unverified","sample_list":"/paper/dlf-disentangled-language-focused-multimodal#ran","syntology_url":"https://syntology.ai/paper/2412.12225","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.12225"}},"official":{"repos":["pwang322/dlf"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/unpaired-multi-domain-histopathology-virtual","slug":"unpaired-multi-domain-histopathology-virtual","title":"Unpaired Multi-Domain Histopathology Virtual Staining using Dual Path Prompted Inversion","date":"2024-12-15","arxiv_id":"2412.11106","repositories_listed":1,"syntology":null},{"url":"/paper/gohd-gaze-oriented-and-highly-disentangled","slug":"gohd-gaze-oriented-and-highly-disentangled","title":"GoHD: Gaze-oriented and Highly Disentangled Portrait Animation with Rhythmic Poses and Realistic Expression","date":"2024-12-12","arxiv_id":"2412.09296","repositories_listed":1,"syntology":null},{"url":"/paper/soft-tensor-product-representations-for-fully","slug":"soft-tensor-product-representations-for-fully","title":"Fully Distributed, Flexible Compositional Visual Representations via Soft Tensor Products","date":"2024-12-05","arxiv_id":"2412.04671","repositories_listed":1,"syntology":{"n":12,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":12,"phrase":"11 ran (of which 0 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) · 1 unverified","sample_list":"/paper/soft-tensor-product-representations-for-fully#ran","syntology_url":"https://syntology.ai/paper/2412.04671","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.04671"}},"official":{"repos":["gomb0c/soft_tpr"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/leveraging-mllm-embeddings-and-attribute","slug":"leveraging-mllm-embeddings-and-attribute","title":"Leveraging MLLM Embeddings and Attribute Smoothing for Compositional Zero-Shot Learning","date":"2024-11-18","arxiv_id":"2411.12584","repositories_listed":1,"syntology":null},{"url":"/paper/ggavatar-reconstructing-garment-separated-3d","slug":"ggavatar-reconstructing-garment-separated-3d","title":"GGAvatar: Reconstructing Garment-Separated 3D Gaussian Splatting Avatars from Monocular Video","date":"2024-11-15","arxiv_id":"2411.09952","repositories_listed":1,"syntology":null},{"url":"/paper/disentangling-tabular-data-towards-better-one","slug":"disentangling-tabular-data-towards-better-one","title":"Disentangling Tabular Data Towards Better One-Class Anomaly Detection","date":"2024-11-12","arxiv_id":"2411.07574","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/disentangling-tabular-data-towards-better-one#ran","syntology_url":"https://syntology.ai/paper/2411.07574","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.07574"}},"official":{"repos":["yjnanan/disent-ad"],"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/fast-disentangled-slim-tensor-learning-for","slug":"fast-disentangled-slim-tensor-learning-for","title":"Fast Disentangled Slim Tensor Learning for Multi-view Clustering","date":"2024-11-12","arxiv_id":"2411.07685","repositories_listed":1,"syntology":null},{"url":"/paper/interaction-asymmetry-a-general-principle-for","slug":"interaction-asymmetry-a-general-principle-for","title":"Interaction Asymmetry: A General Principle for Learning Composable Abstractions","date":"2024-11-12","arxiv_id":"2411.07784","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":10,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/interaction-asymmetry-a-general-principle-for#ran","syntology_url":"https://syntology.ai/paper/2411.07784","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.07784"}},"official":{"repos":["jackbrady/interaction-asymmetry"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/domaingallery-few-shot-domain-driven-image","slug":"domaingallery-few-shot-domain-driven-image","title":"DomainGallery: Few-shot Domain-driven Image Generation by Attribute-centric Finetuning","date":"2024-11-07","arxiv_id":"2411.04571","repositories_listed":1,"syntology":null},{"url":"/paper/collaborative-cognitive-diagnosis-with","slug":"collaborative-cognitive-diagnosis-with","title":"Collaborative Cognitive Diagnosis with Disentangled Representation Learning for Learner Modeling","date":"2024-11-04","arxiv_id":"2411.02066","repositories_listed":1,"syntology":null},{"url":"/paper/an-information-criterion-for-controlled","slug":"an-information-criterion-for-controlled","title":"An Information Criterion for Controlled Disentanglement of Multimodal Data","date":"2024-10-31","arxiv_id":"2410.23996","repositories_listed":1,"syntology":{"n":26,"n_ran":14,"n_constructed":4,"n_ran_checked":11,"n_instrument":3,"n_unverified":12,"n_honours":0,"n_violates":1,"n_no_contract":10,"n_pointer_only":26,"phrase":"14 ran (of which 4 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 1 violated, 10 with no contract checked; 3 where Syntology's instrument failed) · 12 unverified","sample_list":"/paper/an-information-criterion-for-controlled#ran","syntology_url":"https://syntology.ai/paper/2410.23996","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.23996"}},"official":{"repos":["uhlerlab/disentangledssl"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":4,"n_ran_no_instrument_failure":11,"n_unverified":12,"ran_from_kinds":["official"]}}},{"url":"/paper/beyond-accuracy-ensuring-correct-predictions","slug":"beyond-accuracy-ensuring-correct-predictions","title":"Beyond Accuracy: Ensuring Correct Predictions With Correct Rationales","date":"2024-10-31","arxiv_id":"2411.00132","repositories_listed":1,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":7,"n_instrument":6,"n_unverified":1,"n_honours":0,"n_violates":2,"n_no_contract":5,"n_pointer_only":14,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 2 violated, 5 with no contract checked; 6 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/beyond-accuracy-ensuring-correct-predictions#ran","syntology_url":"https://syntology.ai/paper/2411.00132","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.00132"}},"official":{"repos":["deep-real/dcp"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/disentangling-interpretable-factors-with","slug":"disentangling-interpretable-factors-with","title":"Disentangling Interpretable Factors with Supervised Independent Subspace Principal Component Analysis","date":"2024-10-31","arxiv_id":"2410.23595","repositories_listed":1,"syntology":{"n":18,"n_ran":13,"n_constructed":2,"n_ran_checked":7,"n_instrument":6,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"13 ran (of which 2 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 6 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/disentangling-interpretable-factors-with#ran","syntology_url":"https://syntology.ai/paper/2410.23595","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.23595"}},"official":{"repos":["JiayuSuPKU/sispca"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/identifiability-guarantees-for-causal-1","slug":"identifiability-guarantees-for-causal-1","title":"Identifiability Guarantees for Causal Disentanglement from Purely Observational Data","date":"2024-10-31","arxiv_id":"2410.23620","repositories_listed":1,"syntology":null},{"url":"/paper/causaldiff-causality-inspired-disentanglement","slug":"causaldiff-causality-inspired-disentanglement","title":"CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial Defense","date":"2024-10-30","arxiv_id":"2410.23091","repositories_listed":1,"syntology":{"n":17,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"11 ran (of which 0 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) · 6 unverified","sample_list":"/paper/causaldiff-causality-inspired-disentanglement#ran","syntology_url":"https://syntology.ai/paper/2410.23091","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.23091"}},"official":{"repos":["cas-aisafetybasicresearchgroup/causaldiff"],"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":["found_in_text","official"]}}},{"url":"/paper/contrastive-learning-and-adversarial","slug":"contrastive-learning-and-adversarial","title":"Contrastive Learning and Adversarial Disentanglement for Task-Oriented Semantic Communications","date":"2024-10-30","arxiv_id":"2410.22784","repositories_listed":1,"syntology":null},{"url":"/paper/disentangled-and-self-explainable-node","slug":"disentangled-and-self-explainable-node","title":"Disentangled and Self-Explainable Node Representation Learning","date":"2024-10-28","arxiv_id":"2410.21043","repositories_listed":1,"syntology":null},{"url":"/paper/coherence-guided-preference-disentanglement","slug":"coherence-guided-preference-disentanglement","title":"Coherence-guided Preference Disentanglement for Cross-domain Recommendations","date":"2024-10-27","arxiv_id":"2410.20580","repositories_listed":1,"syntology":null},{"url":"/paper/guidance-disentanglement-network-for-optics","slug":"guidance-disentanglement-network-for-optics","title":"Guidance Disentanglement Network for Optics-Guided Thermal UAV Image Super-Resolution","date":"2024-10-27","arxiv_id":"2410.20466","repositories_listed":1,"syntology":null},{"url":"/paper/hico-hierarchical-controllable-diffusion","slug":"hico-hierarchical-controllable-diffusion","title":"HiCo: Hierarchical Controllable Diffusion Model for Layout-to-image Generation","date":"2024-10-18","arxiv_id":"2410.14324","repositories_listed":1,"syntology":{"n":13,"n_ran":13,"n_constructed":0,"n_ran_checked":10,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":13,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hico-hierarchical-controllable-diffusion#ran","syntology_url":"https://syntology.ai/paper/2410.14324","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.14324"}},"official":{"repos":["360cvgroup/hico_t2i"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/counterfactual-generative-modeling-with","slug":"counterfactual-generative-modeling-with","title":"Counterfactual Generative Modeling with Variational Causal Inference","date":"2024-10-16","arxiv_id":"2410.12730","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":3,"n_ran_checked":8,"n_instrument":3,"n_unverified":2,"n_honours":5,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"11 ran (of which 3 constructed an object rather than computing a result; 8 with no instrument failure: 5 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/counterfactual-generative-modeling-with#ran","syntology_url":"https://syntology.ai/paper/2410.12730","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.12730"}},"official":{"repos":["yulun-rayn/variational-causal-inference"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":3,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/meta-dt-offline-meta-rl-as-conditional","slug":"meta-dt-offline-meta-rl-as-conditional","title":"Meta-DT: Offline Meta-RL as Conditional Sequence Modeling with World Model Disentanglement","date":"2024-10-15","arxiv_id":"2410.11448","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":0,"n_no_contract":2,"n_pointer_only":5,"phrase":"3 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; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/meta-dt-offline-meta-rl-as-conditional#ran","syntology_url":"https://syntology.ai/paper/2410.11448","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.11448"}},"official":{"repos":["nju-rl/meta-dt"],"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/textctrl-diffusion-based-scene-text-editing","slug":"textctrl-diffusion-based-scene-text-editing","title":"TextCtrl: Diffusion-based Scene Text Editing with Prior Guidance Control","date":"2024-10-14","arxiv_id":"2410.10133","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":1,"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: 1 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/textctrl-diffusion-based-scene-text-editing#ran","syntology_url":"https://syntology.ai/paper/2410.10133","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.10133"}},"official":{"repos":["weichaozeng/textctrl"],"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/exploring-behavior-relevant-and-disentangled","slug":"exploring-behavior-relevant-and-disentangled","title":"Exploring Behavior-Relevant and Disentangled Neural Dynamics with Generative Diffusion Models","date":"2024-10-12","arxiv_id":"2410.09614","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":3,"n_no_contract":0,"n_pointer_only":5,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 3 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/exploring-behavior-relevant-and-disentangled#ran","syntology_url":"https://syntology.ai/paper/2410.09614","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.09614"}},"official":{"repos":["yulewang97/benediff"],"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","unlocated"]}}},{"url":"/paper/disco-a-hierarchical-disentangled-cognitive","slug":"disco-a-hierarchical-disentangled-cognitive","title":"DISCO: A Hierarchical Disentangled Cognitive Diagnosis Framework for Interpretable Job Recommendation","date":"2024-10-10","arxiv_id":"2410.07671","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/disco-a-hierarchical-disentangled-cognitive#ran","syntology_url":"https://syntology.ai/paper/2410.07671","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.07671"}},"official":{"repos":["LabyrinthineLeo/DISCO"],"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/pixlens-a-novel-framework-for-disentangled","slug":"pixlens-a-novel-framework-for-disentangled","title":"PixLens: A Novel Framework for Disentangled Evaluation in Diffusion-Based Image Editing with Object Detection + SAM","date":"2024-10-08","arxiv_id":"2410.05710","repositories_listed":1,"syntology":null},{"url":"/paper/towards-an-improved-metric-for-evaluating","slug":"towards-an-improved-metric-for-evaluating","title":"Towards an Improved Metric for Evaluating Disentangled Representations","date":"2024-10-04","arxiv_id":"2410.03056","repositories_listed":1,"syntology":null},{"url":"/paper/disentangling-textual-and-acoustic-features","slug":"disentangling-textual-and-acoustic-features","title":"Disentangling Textual and Acoustic Features of Neural Speech Representations","date":"2024-10-03","arxiv_id":"2410.03037","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-contrastive-vae-for","slug":"semi-supervised-contrastive-vae-for","title":"Semi-Supervised Contrastive VAE for Disentanglement of Digital Pathology Images","date":"2024-10-02","arxiv_id":"2410.02012","repositories_listed":1,"syntology":null},{"url":"/paper/unveiling-language-skills-under-circuits","slug":"unveiling-language-skills-under-circuits","title":"Unveiling Language Skills via Path-Level Circuit Discovery","date":"2024-10-02","arxiv_id":"2410.01334","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"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 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) · 1 unverified","sample_list":"/paper/unveiling-language-skills-under-circuits#ran","syntology_url":"https://syntology.ai/paper/2410.01334","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.01334"}},"official":{"repos":["zodiark-ch/language-skill-of-llms"],"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"]}}},{"url":"/paper/magnet-we-never-know-how-text-to-image","slug":"magnet-we-never-know-how-text-to-image","title":"Magnet: We Never Know How Text-to-Image Diffusion Models Work, Until We Learn How Vision-Language Models Function","date":"2024-09-30","arxiv_id":"2409.19967","repositories_listed":1,"syntology":{"n":17,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":6,"n_honours":1,"n_violates":0,"n_no_contract":9,"n_pointer_only":17,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/magnet-we-never-know-how-text-to-image#ran","syntology_url":"https://syntology.ai/paper/2409.19967","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.19967"}},"official":{"repos":["i2-multimedia-lab/magnet"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/transferring-disentangled-representations","slug":"transferring-disentangled-representations","title":"Transferring disentangled representations: bridging the gap between synthetic and real images","date":"2024-09-26","arxiv_id":"2409.18017","repositories_listed":1,"syntology":{"n":13,"n_ran":8,"n_constructed":0,"n_ran_checked":0,"n_instrument":8,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"8 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; 8 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/transferring-disentangled-representations#ran","syntology_url":"https://syntology.ai/paper/2409.18017","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.18017"}},"official":{"repos":["JacopoDapueto/transfer_disentanglement"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/mitigating-semantic-leakage-in-cross-lingual","slug":"mitigating-semantic-leakage-in-cross-lingual","title":"Mitigating Semantic Leakage in Cross-lingual Embeddings via Orthogonality Constraint","date":"2024-09-24","arxiv_id":"2409.15664","repositories_listed":1,"syntology":null},{"url":"/paper/disentanglement-with-factor-quantized","slug":"disentanglement-with-factor-quantized","title":"Disentanglement with Factor Quantized Variational Autoencoders","date":"2024-09-23","arxiv_id":"2409.14851","repositories_listed":1,"syntology":null},{"url":"/paper/disentangling-speakers-in-multi-talker-speech","slug":"disentangling-speakers-in-multi-talker-speech","title":"Disentangling Speakers in Multi-Talker Speech Recognition with Speaker-Aware CTC","date":"2024-09-19","arxiv_id":"2409.12388","repositories_listed":1,"syntology":null},{"url":"/paper/textboost-towards-one-shot-personalization-of","slug":"textboost-towards-one-shot-personalization-of","title":"TextBoost: Towards One-Shot Personalization of Text-to-Image Models via Fine-tuning Text Encoder","date":"2024-09-12","arxiv_id":"2409.08248","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/textboost-towards-one-shot-personalization-of#ran","syntology_url":"https://syntology.ai/paper/2409.08248","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.08248"}},"official":{"repos":["nahyeonkaty/textboost"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/realistic-and-efficient-face-swapping-a","slug":"realistic-and-efficient-face-swapping-a","title":"Realistic and Efficient Face Swapping: A Unified Approach with Diffusion Models","date":"2024-09-11","arxiv_id":"2409.07269","repositories_listed":1,"syntology":null},{"url":"/paper/structure-aware-single-source-generalization","slug":"structure-aware-single-source-generalization","title":"Structure-Aware Single-Source Generalization with Pixel-Level Disentanglement for Joint Optic Disc and Cup Segmentation","date":"2024-09-10","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/celcomen-spatial-causal-disentanglement-for","slug":"celcomen-spatial-causal-disentanglement-for","title":"Celcomen: spatial causal disentanglement for single-cell and tissue perturbation modeling","date":"2024-09-09","arxiv_id":"2409.05804","repositories_listed":1,"syntology":null},{"url":"/paper/cradle-vae-enhancing-single-cell-gene","slug":"cradle-vae-enhancing-single-cell-gene","title":"CRADLE-VAE: Enhancing Single-Cell Gene Perturbation Modeling with Counterfactual Reasoning-based Artifact Disentanglement","date":"2024-09-09","arxiv_id":"2409.05484","repositories_listed":1,"syntology":null},{"url":"/paper/unveiling-advanced-frequency-disentanglement","slug":"unveiling-advanced-frequency-disentanglement","title":"Unveiling Advanced Frequency Disentanglement Paradigm for Low-Light Image Enhancement","date":"2024-09-03","arxiv_id":"2409.01641","repositories_listed":1,"syntology":{"n":15,"n_ran":13,"n_constructed":0,"n_ran_checked":13,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":15,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/unveiling-advanced-frequency-disentanglement#ran","syntology_url":"https://syntology.ai/paper/2409.01641","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.01641"}},"official":{"repos":["redrock303/adf-llie"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/a-joint-learning-model-with-variational","slug":"a-joint-learning-model-with-variational","title":"A Joint Learning Model with Variational Interaction for Multilingual Program Translation","date":"2024-08-25","arxiv_id":"2408.14515","repositories_listed":1,"syntology":null},{"url":"/paper/disentangling-amplifying-and-debiasing","slug":"disentangling-amplifying-and-debiasing","title":"Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural Networks","date":"2024-08-23","arxiv_id":"2408.12875","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-knowledge-tracing-with-concept-map","slug":"enhancing-knowledge-tracing-with-concept-map","title":"Enhancing Knowledge Tracing with Concept Map and Response Disentanglement","date":"2024-08-23","arxiv_id":"2408.12996","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-cross-modal-medical-image","slug":"enhancing-cross-modal-medical-image","title":"Enhancing Cross-Modal Medical Image Segmentation through Compositionality","date":"2024-08-21","arxiv_id":"2408.11733","repositories_listed":1,"syntology":null},{"url":"/paper/fedgs-federated-gradient-scaling-for","slug":"fedgs-federated-gradient-scaling-for","title":"FedGS: Federated Gradient Scaling for Heterogeneous Medical Image Segmentation","date":"2024-08-21","arxiv_id":"2408.11701","repositories_listed":1,"syntology":null},{"url":"/paper/barbie-text-to-barbie-style-3d-avatars","slug":"barbie-text-to-barbie-style-3d-avatars","title":"Barbie: Text to Barbie-Style 3D Avatars","date":"2024-08-17","arxiv_id":"2408.09126","repositories_listed":1,"syntology":null},{"url":"/paper/modeling-the-neonatal-brain-development-using","slug":"modeling-the-neonatal-brain-development-using","title":"Modeling the Neonatal Brain Development Using Implicit Neural Representations","date":"2024-08-16","arxiv_id":"2408.08647","repositories_listed":1,"syntology":null},{"url":"/paper/crocodile-causality-aids-robustness-via","slug":"crocodile-causality-aids-robustness-via","title":"CROCODILE: Causality aids RObustness via COntrastive DIsentangled LEarning","date":"2024-08-09","arxiv_id":"2408.04949","repositories_listed":1,"syntology":null},{"url":"/paper/2408-03208","slug":"2408-03208","title":"Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic Surgery","date":"2024-08-06","arxiv_id":"2408.03208","repositories_listed":1,"syntology":null},{"url":"/paper/extend-model-merging-from-fine-tuned-to-pre","slug":"extend-model-merging-from-fine-tuned-to-pre","title":"Extend Model Merging from Fine-Tuned to Pre-Trained Large Language Models via Weight Disentanglement","date":"2024-08-06","arxiv_id":"2408.03092","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":5,"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) · 0 unverified","sample_list":"/paper/extend-model-merging-from-fine-tuned-to-pre#ran","syntology_url":"https://syntology.ai/paper/2408.03092","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.03092"}},"official":{"repos":["yule-BUAA/MergeLLM"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/emotion-driven-piano-music-generation-via-two","slug":"emotion-driven-piano-music-generation-via-two","title":"Emotion-driven Piano Music Generation via Two-stage Disentanglement and Functional Representation","date":"2024-07-30","arxiv_id":"2407.20955","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-effects-of-irrelevant-variables-in","slug":"on-the-effects-of-irrelevant-variables-in","title":"On the Effects of Irrelevant Variables in Treatment Effect Estimation with Deep Disentanglement","date":"2024-07-29","arxiv_id":"2407.20003","repositories_listed":1,"syntology":null},{"url":"/paper/mamba-catch-the-hype-or-rethink-what-really","slug":"mamba-catch-the-hype-or-rethink-what-really","title":"Mamba? Catch The Hype Or Rethink What Really Helps for Image Registration","date":"2024-07-27","arxiv_id":"2407.19274","repositories_listed":1,"syntology":null},{"url":"/paper/artist-aesthetically-controllable-text-driven","slug":"artist-aesthetically-controllable-text-driven","title":"DiffArtist: Towards Structure and Appearance Controllable Image Stylization","date":"2024-07-22","arxiv_id":"2407.15842","repositories_listed":1,"syntology":null},{"url":"/paper/disentangling-spatio-temporal-knowledge-for","slug":"disentangling-spatio-temporal-knowledge-for","title":"Disentangling spatio-temporal knowledge for weakly supervised object detection and segmentation in surgical video","date":"2024-07-22","arxiv_id":"2407.15794","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-acquisition-disentanglement-for","slug":"knowledge-acquisition-disentanglement-for","title":"Knowledge Acquisition Disentanglement for Knowledge-based Visual Question Answering with Large Language Models","date":"2024-07-22","arxiv_id":"2407.15346","repositories_listed":1,"syntology":null},{"url":"/paper/a-new-dataset-and-framework-for-real-world","slug":"a-new-dataset-and-framework-for-real-world","title":"A New Dataset and Framework for Real-World Blurred Images Super-Resolution","date":"2024-07-20","arxiv_id":"2407.14880","repositories_listed":1,"syntology":null},{"url":"/paper/orthogonal-hyper-category-guided-multi","slug":"orthogonal-hyper-category-guided-multi","title":"Orthogonal Hyper-category Guided Multi-interest Elicitation for Micro-video Matching","date":"2024-07-20","arxiv_id":"2407.14741","repositories_listed":1,"syntology":null},{"url":"/paper/sa-dvae-improving-zero-shot-skeleton-based","slug":"sa-dvae-improving-zero-shot-skeleton-based","title":"SA-DVAE: Improving Zero-Shot Skeleton-Based Action Recognition by Disentangled Variational Autoencoders","date":"2024-07-18","arxiv_id":"2407.13460","repositories_listed":1,"syntology":null},{"url":"/paper/practical-unlearning-for-large-language","slug":"practical-unlearning-for-large-language","title":"On Large Language Model Continual Unlearning","date":"2024-07-14","arxiv_id":"2407.10223","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":7,"n_instrument":5,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":13,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/practical-unlearning-for-large-language#ran","syntology_url":"https://syntology.ai/paper/2407.10223","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.10223"}},"official":{"repos":["gcyzsl/o3-llm-unlearning"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/samoye-zero-shot-singing-voice-conversion","slug":"samoye-zero-shot-singing-voice-conversion","title":"SaMoye: Zero-shot Singing Voice Conversion Model Based on Feature Disentanglement and Enhancement","date":"2024-07-10","arxiv_id":"2407.07728","repositories_listed":1,"syntology":null},{"url":"/paper/colorpeel-color-prompt-learning-with","slug":"colorpeel-color-prompt-learning-with","title":"ColorPeel: Color Prompt Learning with Diffusion Models via Color and Shape Disentanglement","date":"2024-07-09","arxiv_id":"2407.07197","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/colorpeel-color-prompt-learning-with#ran","syntology_url":"https://syntology.ai/paper/2407.07197","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.07197"}},"official":{"repos":["moatifbutt/color-peel"],"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/general-and-task-oriented-video-segmentation","slug":"general-and-task-oriented-video-segmentation","title":"General and Task-Oriented Video Segmentation","date":"2024-07-09","arxiv_id":"2407.06540","repositories_listed":1,"syntology":null},{"url":"/paper/deciphering-the-role-of-representation","slug":"deciphering-the-role-of-representation","title":"Deciphering the Role of Representation Disentanglement: Investigating Compositional Generalization in CLIP Models","date":"2024-07-08","arxiv_id":"2407.05897","repositories_listed":1,"syntology":null},{"url":"/paper/completed-feature-disentanglement-learning","slug":"completed-feature-disentanglement-learning","title":"Completed Feature Disentanglement Learning for Multimodal MRIs Analysis","date":"2024-07-06","arxiv_id":"2407.04916","repositories_listed":1,"syntology":null}],"record_sha256":"3f7750a0e3c00aa7e4fbe3259b289a71098389da2e37bced8195fbb515fa108b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}