{"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/self-supervised-learning/papers/20","list_of":"/task/self-supervised-learning","task":"Self-Supervised Learning","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":20,"pages_in_order":51,"rows_per_page":100,"rows":[1901,2000],"of":5044,"counts":{"archive_papers_tagged":5044,"with_a_code_link":2293,"where_syntology_ran_a_sample":666,"not_listed_spam_title":0,"listed":5044,"listed_where_code_ran":666,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":581,"every_run_a_failure_of_syntologys_instrument":85,"listed_with_a_run_with_no_instrument_failure":581,"listed_every_run_a_failure_of_syntologys_instrument":85,"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/self-supervised-learning","prev":"/task/self-supervised-learning/papers/19","next":"/task/self-supervised-learning/papers/21","papers":[{"url":"/paper/self-supervised-learning-to-prove-equivalence","slug":"self-supervised-learning-to-prove-equivalence","title":"Self-Supervised Learning to Prove Equivalence Between Straight-Line Programs via Rewrite Rules","date":"2021-09-22","arxiv_id":"2109.10476","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-methods-and","slug":"self-supervised-learning-methods-and","title":"Self-supervised learning methods and applications in medical imaging analysis: A survey","date":"2021-09-17","arxiv_id":"2109.08685","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-neural-architecture-search-1","slug":"self-supervised-neural-architecture-search-1","title":"Self-Supervised Neural Architecture Search for Imbalanced Datasets","date":"2021-09-17","arxiv_id":"2109.08580","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-contrastive-learning-for-eeg","slug":"self-supervised-contrastive-learning-for-eeg","title":"Self-supervised Contrastive Learning for EEG-based Sleep Staging","date":"2021-09-16","arxiv_id":"2109.07839","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/self-supervised-contrastive-learning-for-eeg#ran","syntology_url":"https://syntology.ai/paper/2109.07839","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.07839"}},"official":{"repos":["xuejiang16/ssl-torch"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/solving-occlusion-in-terrain-mapping-with","slug":"solving-occlusion-in-terrain-mapping-with","title":"Reconstructing occluded Elevation Information in Terrain Maps with Self-supervised Learning","date":"2021-09-15","arxiv_id":"2109.07150","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-metric-learning-with-graph","slug":"self-supervised-metric-learning-with-graph","title":"Self-Supervised Metric Learning With Graph Clustering For Speaker Diarization","date":"2021-09-14","arxiv_id":"2109.06824","repositories_listed":1,"syntology":null},{"url":"/paper/online-unsupervised-learning-of-visual","slug":"online-unsupervised-learning-of-visual","title":"Online Unsupervised Learning of Visual Representations and Categories","date":"2021-09-13","arxiv_id":"2109.05675","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/online-unsupervised-learning-of-visual#ran","syntology_url":"https://syntology.ai/paper/2109.05675","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.05675"}},"official":{"repos":["renmengye/online-unsup-proto-net"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/teasel-a-transformer-based-speech-prefixed","slug":"teasel-a-transformer-based-speech-prefixed","title":"TEASEL: A Transformer-Based Speech-Prefixed Language Model","date":"2021-09-12","arxiv_id":"2109.05522","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/teasel-a-transformer-based-speech-prefixed#ran","syntology_url":"https://syntology.ai/paper/2109.05522","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.05522"}},"official":null}},{"url":"/paper/attention-based-contrastive-learning-for","slug":"attention-based-contrastive-learning-for","title":"Attention-based Contrastive Learning for Winograd Schemas","date":"2021-09-10","arxiv_id":"2109.05108","repositories_listed":1,"syntology":null},{"url":"/paper/spatio-temporal-recurrent-networks-for-event","slug":"spatio-temporal-recurrent-networks-for-event","title":"Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation","date":"2021-09-10","arxiv_id":"2109.04871","repositories_listed":1,"syntology":{"n":11,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":1,"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) · 0 unverified","sample_list":"/paper/spatio-temporal-recurrent-networks-for-event#ran","syntology_url":"https://syntology.ai/paper/2109.04871","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.04871"}},"official":{"repos":["ruizhao26/ste-flownet"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/topic-aware-contrastive-learning-for","slug":"topic-aware-contrastive-learning-for","title":"Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization","date":"2021-09-10","arxiv_id":"2109.04994","repositories_listed":1,"syntology":null},{"url":"/paper/towards-zero-shot-commonsense-reasoning-with","slug":"towards-zero-shot-commonsense-reasoning-with","title":"Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models","date":"2021-09-10","arxiv_id":"2109.05105","repositories_listed":1,"syntology":null},{"url":"/paper/taming-self-supervised-learning-for","slug":"taming-self-supervised-learning-for","title":"Taming Self-Supervised Learning for Presentation Attack Detection: De-Folding and De-Mixing","date":"2021-09-09","arxiv_id":"2109.04100","repositories_listed":1,"syntology":null},{"url":"/paper/fine-grained-hand-gesture-recognition-in","slug":"fine-grained-hand-gesture-recognition-in","title":"Fine-grained Hand Gesture Recognition in Multi-viewpoint Hand Hygiene","date":"2021-09-07","arxiv_id":"2109.02917","repositories_listed":1,"syntology":null},{"url":"/paper/training-deep-networks-from-zero-to-hero","slug":"training-deep-networks-from-zero-to-hero","title":"Training Deep Networks from Zero to Hero: avoiding pitfalls and going beyond","date":"2021-09-06","arxiv_id":"2109.02752","repositories_listed":1,"syntology":null},{"url":"/paper/re-entry-prediction-for-online-conversations","slug":"re-entry-prediction-for-online-conversations","title":"Re-entry Prediction for Online Conversations via Self-Supervised Learning","date":"2021-09-05","arxiv_id":"2109.02020","repositories_listed":1,"syntology":null},{"url":"/paper/improving-joint-learning-of-chest-x-ray-and","slug":"improving-joint-learning-of-chest-x-ray-and","title":"Improving Joint Learning of Chest X-Ray and Radiology Report by Word Region Alignment","date":"2021-09-04","arxiv_id":"2109.01949","repositories_listed":1,"syntology":null},{"url":"/paper/laviter-learning-aligned-visual-and-textual","slug":"laviter-learning-aligned-visual-and-textual","title":"LAViTeR: Learning Aligned Visual and Textual Representations Assisted by Image and Caption Generation","date":"2021-09-04","arxiv_id":"2109.04993","repositories_listed":1,"syntology":null},{"url":"/paper/multi-centred-strong-augmentation-via","slug":"multi-centred-strong-augmentation-via","title":"Self-supervised Pseudo Multi-class Pre-training for Unsupervised Anomaly Detection and Segmentation in Medical Images","date":"2021-09-03","arxiv_id":"2109.01303","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-recommendation-with-cross","slug":"self-supervised-recommendation-with-cross","title":"Motifs-based Recommender System via Hypergraph Convolution and Contrastive Learning","date":"2021-09-02","arxiv_id":"2109.00676","repositories_listed":1,"syntology":null},{"url":"/paper/vibcreg-variance-invariance-better-covariance","slug":"vibcreg-variance-invariance-better-covariance","title":"Computer Vision Self-supervised Learning Methods on Time Series","date":"2021-09-02","arxiv_id":"2109.00783","repositories_listed":1,"syntology":null},{"url":"/paper/point-cloud-pre-training-by-mixing-and","slug":"point-cloud-pre-training-by-mixing-and","title":"Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes","date":"2021-09-01","arxiv_id":"2109.00452","repositories_listed":1,"syntology":null},{"url":"/paper/scatsimclr-self-supervised-contrastive","slug":"scatsimclr-self-supervised-contrastive","title":"ScatSimCLR: self-supervised contrastive learning with pretext task regularization for small-scale datasets","date":"2021-08-31","arxiv_id":"2108.13939","repositories_listed":1,"syntology":null},{"url":"/paper/digging-into-uncertainty-in-self-supervised","slug":"digging-into-uncertainty-in-self-supervised","title":"Digging into Uncertainty in Self-supervised Multi-view Stereo","date":"2021-08-30","arxiv_id":"2108.12966","repositories_listed":1,"syntology":{"n":5,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 5 unverified","sample_list":"/paper/digging-into-uncertainty-in-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2108.12966","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.12966"}},"official":{"repos":["toughstonex/u-mvs"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":5,"ran_from_kinds":[]}}},{"url":"/paper/multisiam-self-supervised-multi-instance","slug":"multisiam-self-supervised-multi-instance","title":"MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Driving","date":"2021-08-27","arxiv_id":"2108.12178","repositories_listed":1,"syntology":null},{"url":"/paper/generative-and-contrastive-self-supervised","slug":"generative-and-contrastive-self-supervised","title":"Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection","date":"2021-08-23","arxiv_id":"2108.09896","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/generative-and-contrastive-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2108.09896","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.09896"}},"official":null}},{"url":"/paper/jointly-learnable-data-augmentations-for-self","slug":"jointly-learnable-data-augmentations-for-self","title":"Jointly Learnable Data Augmentations for Self-Supervised GNNs","date":"2021-08-23","arxiv_id":"2108.10420","repositories_listed":1,"syntology":null},{"url":"/paper/self-rule-to-adapt-generalized-multi-source","slug":"self-rule-to-adapt-generalized-multi-source","title":"Self-Rule to Multi-Adapt: Generalized Multi-source Feature Learning Using Unsupervised Domain Adaptation for Colorectal Cancer Tissue Detection","date":"2021-08-20","arxiv_id":"2108.09178","repositories_listed":1,"syntology":null},{"url":"/paper/topo2vec-topography-embedding-using-the","slug":"topo2vec-topography-embedding-using-the","title":"Topo2vec: Topography Embedding Using the Fractal Effect","date":"2021-08-19","arxiv_id":"2108.08870","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-3d-human-pose-estimation-with","slug":"self-supervised-3d-human-pose-estimation-with","title":"Self-Supervised 3D Human Pose Estimation with Multiple-View Geometry","date":"2021-08-17","arxiv_id":"2108.07777","repositories_listed":1,"syntology":null},{"url":"/paper/collaborative-unsupervised-visual","slug":"collaborative-unsupervised-visual","title":"Collaborative Unsupervised Visual Representation Learning from Decentralized Data","date":"2021-08-14","arxiv_id":"2108.06492","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/collaborative-unsupervised-visual#ran","syntology_url":"https://syntology.ai/paper/2108.06492","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.06492"}},"official":{"repos":["EasyFL-AI/EasyFL"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/contrastive-self-supervised-sequential","slug":"contrastive-self-supervised-sequential","title":"Contrastive Self-supervised Sequential Recommendation with Robust Augmentation","date":"2021-08-14","arxiv_id":"2108.06479","repositories_listed":1,"syntology":null},{"url":"/paper/focus-on-the-positives-self-supervised","slug":"focus-on-the-positives-self-supervised","title":"Focus on the Positives: Self-Supervised Learning for Biodiversity Monitoring","date":"2021-08-14","arxiv_id":"2108.06435","repositories_listed":1,"syntology":null},{"url":"/paper/dual-path-learning-for-domain-adaptation-of","slug":"dual-path-learning-for-domain-adaptation-of","title":"Dual Path Learning for Domain Adaptation of Semantic Segmentation","date":"2021-08-13","arxiv_id":"2108.06337","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"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) · 3 unverified","sample_list":"/paper/dual-path-learning-for-domain-adaptation-of#ran","syntology_url":"https://syntology.ai/paper/2108.06337","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.06337"}},"official":{"repos":["royee182/dpl"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/ammus-a-survey-of-transformer-based","slug":"ammus-a-survey-of-transformer-based","title":"AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing","date":"2021-08-12","arxiv_id":"2108.05542","repositories_listed":1,"syntology":null},{"url":"/paper/cervical-optical-coherence-tomography-image","slug":"cervical-optical-coherence-tomography-image","title":"Cervical Optical Coherence Tomography Image Classification Based on Contrastive Self-Supervised Texture Learning","date":"2021-08-11","arxiv_id":"2108.05081","repositories_listed":1,"syntology":null},{"url":"/paper/representation-learning-for-remote-sensing-an","slug":"representation-learning-for-remote-sensing-an","title":"Representation Learning for Remote Sensing: An Unsupervised Sensor Fusion Approach","date":"2021-08-11","arxiv_id":"2108.05094","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/representation-learning-for-remote-sensing-an#ran","syntology_url":"https://syntology.ai/paper/2108.05094","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.05094"}},"official":{"repos":["descarteslabs/contrastive_sensor_fusion"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-consensus-representation","slug":"self-supervised-consensus-representation","title":"Self-supervised Consensus Representation Learning for Attributed Graph","date":"2021-08-10","arxiv_id":"2108.04822","repositories_listed":1,"syntology":null},{"url":"/paper/skeleton-contrastive-3d-action-representation","slug":"skeleton-contrastive-3d-action-representation","title":"Skeleton-Contrastive 3D Action Representation Learning","date":"2021-08-08","arxiv_id":"2108.03656","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":7,"phrase":"4 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; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/skeleton-contrastive-3d-action-representation#ran","syntology_url":"https://syntology.ai/paper/2108.03656","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.03656"}},"official":{"repos":["fmthoker/skeleton-contrast"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/improving-contrastive-learning-by-visualizing","slug":"improving-contrastive-learning-by-visualizing","title":"Improving Contrastive Learning by Visualizing Feature Transformation","date":"2021-08-06","arxiv_id":"2108.02982","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"0 ran · 2 unverified","sample_list":"/paper/improving-contrastive-learning-by-visualizing#ran","syntology_url":"https://syntology.ai/paper/2108.02982","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.02982"}},"official":{"repos":["DTennant/CL-Visualizing-Feature-Transformation"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/co-learning-learning-from-noisy-labels-with","slug":"co-learning-learning-from-noisy-labels-with","title":"Co-learning: Learning from Noisy Labels with Self-supervision","date":"2021-08-05","arxiv_id":"2108.04063","repositories_listed":1,"syntology":null},{"url":"/paper/object-aware-contrastive-learning-for","slug":"object-aware-contrastive-learning-for","title":"Object-aware Contrastive Learning for Debiased Scene Representation","date":"2021-07-30","arxiv_id":"2108.00049","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-for-fine-grained-1","slug":"self-supervised-learning-for-fine-grained-1","title":"Self-Supervised Learning for Fine-Grained Image Classification","date":"2021-07-29","arxiv_id":"2107.13973","repositories_listed":1,"syntology":null},{"url":"/paper/ccgl-contrastive-cascade-graph-learning","slug":"ccgl-contrastive-cascade-graph-learning","title":"CCGL: Contrastive Cascade Graph Learning","date":"2021-07-27","arxiv_id":"2107.12576","repositories_listed":1,"syntology":null},{"url":"/paper/disentangled-implicit-shape-and-pose-learning","slug":"disentangled-implicit-shape-and-pose-learning","title":"DISP6D: Disentangled Implicit Shape and Pose Learning for Scalable 6D Pose Estimation","date":"2021-07-27","arxiv_id":"2107.12549","repositories_listed":1,"syntology":null},{"url":"/paper/aavae-augmentation-augmented-variational","slug":"aavae-augmentation-augmented-variational","title":"AASAE: Augmentation-Augmented Stochastic Autoencoders","date":"2021-07-26","arxiv_id":"2107.12329","repositories_listed":1,"syntology":null},{"url":"/paper/hand-image-understanding-via-deep-multi-task","slug":"hand-image-understanding-via-deep-multi-task","title":"Hand Image Understanding via Deep Multi-Task Learning","date":"2021-07-24","arxiv_id":"2107.11646","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":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) · 1 unverified","sample_list":"/paper/hand-image-understanding-via-deep-multi-task#ran","syntology_url":"https://syntology.ai/paper/2107.11646","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.11646"}},"official":{"repos":["MandyMo/HIU-DMTL"],"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/triplet-is-all-you-need-with-random-mappings","slug":"triplet-is-all-you-need-with-random-mappings","title":"Trip-ROMA: Self-Supervised Learning with Triplets and Random Mappings","date":"2021-07-22","arxiv_id":"2107.10419","repositories_listed":1,"syntology":null},{"url":"/paper/deepsmile-self-supervised-heterogeneity-aware","slug":"deepsmile-self-supervised-heterogeneity-aware","title":"DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer","date":"2021-07-20","arxiv_id":"2107.09405","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/deepsmile-self-supervised-heterogeneity-aware#ran","syntology_url":"https://syntology.ai/paper/2107.09405","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.09405"}},"official":{"repos":["nki-ai/dlup-lightning-mil"],"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/ccvs-context-aware-controllable-video","slug":"ccvs-context-aware-controllable-video","title":"CCVS: Context-aware Controllable Video Synthesis","date":"2021-07-16","arxiv_id":"2107.08037","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":5,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 1 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/ccvs-context-aware-controllable-video#ran","syntology_url":"https://syntology.ai/paper/2107.08037","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.08037"}},"official":{"repos":["16lemoing/ccvs"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-learning-framework-for-remote","slug":"self-supervised-learning-framework-for-remote","title":"Self-supervised Representation Learning Framework for Remote Physiological Measurement Using Spatiotemporal Augmentation Loss","date":"2021-07-16","arxiv_id":"2107.07695","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/self-supervised-learning-framework-for-remote#ran","syntology_url":"https://syntology.ai/paper/2107.07695","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.07695"}},"official":{"repos":["Dylan-H-Wang/SLF-RPM"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/eproduct-a-million-scale-visual-search","slug":"eproduct-a-million-scale-visual-search","title":"eProduct: A Million-Scale Visual Search Benchmark to Address Product Recognition Challenges","date":"2021-07-13","arxiv_id":"2107.05856","repositories_listed":1,"syntology":null},{"url":"/paper/kit-net-self-supervised-learning-to-kit-novel","slug":"kit-net-self-supervised-learning-to-kit-novel","title":"Kit-Net: Self-Supervised Learning to Kit Novel 3D Objects into Novel 3D Cavities","date":"2021-07-13","arxiv_id":"2107.05789","repositories_listed":1,"syntology":null},{"url":"/paper/on-designing-good-representation-learning","slug":"on-designing-good-representation-learning","title":"Clustering-Based Representation Learning through Output Translation and Its Application to Remote--Sensing Images","date":"2021-07-13","arxiv_id":"2107.05948","repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-multi-modal-video-temporal","slug":"end-to-end-multi-modal-video-temporal","title":"End-to-end Multi-modal Video Temporal Grounding","date":"2021-07-12","arxiv_id":"2107.05624","repositories_listed":1,"syntology":null},{"url":"/paper/layer-wise-analysis-of-a-self-supervised","slug":"layer-wise-analysis-of-a-self-supervised","title":"Layer-wise Analysis of a Self-supervised Speech Representation Model","date":"2021-07-10","arxiv_id":"2107.04734","repositories_listed":1,"syntology":null},{"url":"/paper/investigate-the-essence-of-long-tailed","slug":"investigate-the-essence-of-long-tailed","title":"Investigate the Essence of Long-Tailed Recognition from a Unified Perspective","date":"2021-07-08","arxiv_id":"2107.03758","repositories_listed":1,"syntology":null},{"url":"/paper/continual-contrastive-self-supervised","slug":"continual-contrastive-self-supervised","title":"Continual Contrastive Learning for Image Classification","date":"2021-07-05","arxiv_id":"2107.01776","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"4 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/continual-contrastive-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2107.01776","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.01776"}},"official":{"repos":["VDIGPKU/ContinualContrastiveLearning"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-a-model-for-inferring-a-spatial-road","slug":"learning-a-model-for-inferring-a-spatial-road","title":"Learning a Model for Inferring a Spatial Road Lane Network Graph using Self-Supervision","date":"2021-07-05","arxiv_id":"2107.01784","repositories_listed":1,"syntology":null},{"url":"/paper/bag-of-instances-aggregation-boosts-self","slug":"bag-of-instances-aggregation-boosts-self","title":"Bag of Instances Aggregation Boosts Self-supervised Distillation","date":"2021-07-04","arxiv_id":"2107.01691","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/bag-of-instances-aggregation-boosts-self#ran","syntology_url":"https://syntology.ai/paper/2107.01691","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.01691"}},"official":{"repos":["haohang96/bingo"],"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/mixed-supervision-learning-for-whole-slide","slug":"mixed-supervision-learning-for-whole-slide","title":"Hybrid Supervision Learning for Pathology Whole Slide Image Classification","date":"2021-07-02","arxiv_id":"2107.00934","repositories_listed":1,"syntology":null},{"url":"/paper/pretext-tasks-selection-for-multitask-self","slug":"pretext-tasks-selection-for-multitask-self","title":"Pretext Tasks selection for multitask self-supervised speech representation learning","date":"2021-07-01","arxiv_id":"2107.00594","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pretext-tasks-selection-for-multitask-self#ran","syntology_url":"https://syntology.ai/paper/2107.00594","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.00594"}},"official":{"repos":["salah-zaiem/PL-groupselection"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/as-easy-as-apc-leveraging-self-supervised","slug":"as-easy-as-apc-leveraging-self-supervised","title":"As easy as APC: overcoming missing data and class imbalance in time series with self-supervised learning","date":"2021-06-29","arxiv_id":"2106.15577","repositories_listed":1,"syntology":null},{"url":"/paper/autonovel-automatically-discovering-and","slug":"autonovel-automatically-discovering-and","title":"AutoNovel: Automatically Discovering and Learning Novel Visual Categories","date":"2021-06-29","arxiv_id":"2106.15252","repositories_listed":1,"syntology":null},{"url":"/paper/litegem-lite-geometry-enhanced-molecular","slug":"litegem-lite-geometry-enhanced-molecular","title":"LiteGEM: Lite Geometry Enhanced Molecular Representation Learning for Quantum Property Prediction","date":"2021-06-28","arxiv_id":"2106.14494","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-monocular-depth-estimation-of","slug":"self-supervised-monocular-depth-estimation-of","title":"Self-Supervised Monocular Depth Estimation of Untextured Indoor Rotated Scenes","date":"2021-06-24","arxiv_id":"2106.12958","repositories_listed":1,"syntology":null},{"url":"/paper/stress-super-resolution-for-dynamic-fetal-mri","slug":"stress-super-resolution-for-dynamic-fetal-mri","title":"STRESS: Super-Resolution for Dynamic Fetal MRI using Self-Supervised Learning","date":"2021-06-23","arxiv_id":"2106.12407","repositories_listed":1,"syntology":null},{"url":"/paper/credal-self-supervised-learning","slug":"credal-self-supervised-learning","title":"Credal Self-Supervised Learning","date":"2021-06-22","arxiv_id":"2106.11853","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/credal-self-supervised-learning#ran","syntology_url":"https://syntology.ai/paper/2106.11853","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.11853"}},"official":{"repos":["julilien/cssl"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/unsupervised-object-level-representation","slug":"unsupervised-object-level-representation","title":"Unsupervised Object-Level Representation Learning from Scene Images","date":"2021-06-22","arxiv_id":"2106.11952","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/unsupervised-object-level-representation#ran","syntology_url":"https://syntology.ai/paper/2106.11952","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.11952"}},"official":{"repos":["jiahao000/orl"],"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/remote-sensing-images-semantic-segmentation","slug":"remote-sensing-images-semantic-segmentation","title":"Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images","date":"2021-06-20","arxiv_id":"2106.10605","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"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; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/remote-sensing-images-semantic-segmentation#ran","syntology_url":"https://syntology.ai/paper/2106.10605","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.10605"}},"official":{"repos":["GeoX-Lab/G-RSIM"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/prototype-augmentation-and-self-supervision","slug":"prototype-augmentation-and-self-supervision","title":"Prototype Augmentation and Self-Supervision for Incremental Learning","date":"2021-06-19","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/safe-local-motion-planning-with-self","slug":"safe-local-motion-planning-with-self","title":"Safe Local Motion Planning With Self-Supervised Freespace Forecasting","date":"2021-06-19","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-video-representation-learning-7","slug":"self-supervised-video-representation-learning-7","title":"Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrasting","date":"2021-06-18","arxiv_id":"2106.10137","repositories_listed":1,"syntology":null},{"url":"/paper/a-random-cnn-sees-objects-one-inductive-bias","slug":"a-random-cnn-sees-objects-one-inductive-bias","title":"A Random CNN Sees Objects: One Inductive Bias of CNN and Its Applications","date":"2021-06-17","arxiv_id":"2106.09259","repositories_listed":1,"syntology":null},{"url":"/paper/a-self-supervised-method-for-entity-alignment","slug":"a-self-supervised-method-for-entity-alignment","title":"A Self-supervised Method for Entity Alignment","date":"2021-06-17","arxiv_id":"2106.09395","repositories_listed":1,"syntology":null},{"url":"/paper/augmented-tensor-decomposition-with","slug":"augmented-tensor-decomposition-with","title":"ATD: Augmenting CP Tensor Decomposition by Self Supervision","date":"2021-06-15","arxiv_id":"2106.07900","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":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/augmented-tensor-decomposition-with#ran","syntology_url":"https://syntology.ai/paper/2106.07900","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.07900"}},"official":{"repos":["ycq091044/atd"],"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/self-supervised-learning-with-kernel","slug":"self-supervised-learning-with-kernel","title":"Self-Supervised Learning with Kernel Dependence Maximization","date":"2021-06-15","arxiv_id":"2106.08320","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/self-supervised-learning-with-kernel#ran","syntology_url":"https://syntology.ai/paper/2106.08320","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.08320"}},"official":{"repos":["deepmind/ssl_hsic"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/delving-deep-into-the-generalization-of","slug":"delving-deep-into-the-generalization-of","title":"Delving Deep into the Generalization of Vision Transformers under Distribution Shifts","date":"2021-06-14","arxiv_id":"2106.07617","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/delving-deep-into-the-generalization-of#ran","syntology_url":"https://syntology.ai/paper/2106.07617","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.07617"}},"official":{"repos":["Phoenix1153/ViT_OOD_generalization"],"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/latent-correlation-based-multiview-learning","slug":"latent-correlation-based-multiview-learning","title":"Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability Perspective","date":"2021-06-14","arxiv_id":"2106.07115","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/latent-correlation-based-multiview-learning#ran","syntology_url":"https://syntology.ai/paper/2106.07115","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.07115"}},"official":null}},{"url":"/paper/sas-self-augmented-strategy-for-language","slug":"sas-self-augmented-strategy-for-language","title":"SAS: Self-Augmentation Strategy for Language Model Pre-training","date":"2021-06-14","arxiv_id":"2106.07176","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-graph-augmentation-to-improve","slug":"adversarial-graph-augmentation-to-improve","title":"Adversarial Graph Augmentation to Improve Graph Contrastive Learning","date":"2021-06-10","arxiv_id":"2106.05819","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/adversarial-graph-augmentation-to-improve#ran","syntology_url":"https://syntology.ai/paper/2106.05819","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.05819"}},"official":null}},{"url":"/paper/automated-self-supervised-learning-for-graphs","slug":"automated-self-supervised-learning-for-graphs","title":"Automated Self-Supervised Learning for Graphs","date":"2021-06-10","arxiv_id":"2106.05470","repositories_listed":1,"syntology":null},{"url":"/paper/cross-domain-contrastive-learning-for","slug":"cross-domain-contrastive-learning-for","title":"Cross-domain Contrastive Learning for Unsupervised Domain Adaptation","date":"2021-06-10","arxiv_id":"2106.05528","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-graph-learning-with","slug":"self-supervised-graph-learning-with","title":"Self-Supervised Graph Learning with Hyperbolic Embedding for Temporal Health Event Prediction","date":"2021-06-09","arxiv_id":"2106.04751","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":1,"n_instrument":4,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/self-supervised-graph-learning-with#ran","syntology_url":"https://syntology.ai/paper/2106.04751","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.04751"}},"official":{"repos":["LuChang-CS/sherbet"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/interpretable-agent-communication-from","slug":"interpretable-agent-communication-from","title":"Interpretable agent communication from scratch (with a generic visual processor emerging on the side)","date":"2021-06-08","arxiv_id":"2106.04258","repositories_listed":1,"syntology":null},{"url":"/paper/interpretable-and-low-resource-entity","slug":"interpretable-and-low-resource-entity","title":"Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making","date":"2021-06-08","arxiv_id":"2106.04174","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/interpretable-and-low-resource-entity#ran","syntology_url":"https://syntology.ai/paper/2106.04174","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.04174"}},"official":{"repos":["THU-KEG/HIF-KAT"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/self-supervised-learning-with-data","slug":"self-supervised-learning-with-data","title":"Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style","date":"2021-06-08","arxiv_id":"2106.04619","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/self-supervised-learning-with-data#ran","syntology_url":"https://syntology.ai/paper/2106.04619","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.04619"}},"official":{"repos":["ysharma1126/ssl_identifiability"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-structure-from-motion-through","slug":"self-supervised-structure-from-motion-through","title":"On the Coupling of Depth and Egomotion Networks for Self-Supervised Structure from Motion","date":"2021-06-07","arxiv_id":"2106.04007","repositories_listed":1,"syntology":null},{"url":"/paper/socially-aware-self-supervised-tri-training","slug":"socially-aware-self-supervised-tri-training","title":"Socially-Aware Self-Supervised Tri-Training for Recommendation","date":"2021-06-07","arxiv_id":"2106.03569","repositories_listed":1,"syntology":null},{"url":"/paper/source-free-open-compound-domain-adaptation","slug":"source-free-open-compound-domain-adaptation","title":"Source-Free Open Compound Domain Adaptation in Semantic Segmentation","date":"2021-06-07","arxiv_id":"2106.03422","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-rubik-s-cube-solver","slug":"self-supervised-rubik-s-cube-solver","title":"Self-Supervision is All You Need for Solving Rubik's Cube","date":"2021-06-06","arxiv_id":"2106.03157","repositories_listed":1,"syntology":null},{"url":"/paper/graph-barlow-twins-a-self-supervised","slug":"graph-barlow-twins-a-self-supervised","title":"Graph Barlow Twins: A self-supervised representation learning framework for graphs","date":"2021-06-04","arxiv_id":"2106.02466","repositories_listed":1,"syntology":null},{"url":"/paper/zerowaste-dataset-towards-automated-waste","slug":"zerowaste-dataset-towards-automated-waste","title":"ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes","date":"2021-06-04","arxiv_id":"2106.02740","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-self-supervised-learning-for-out","slug":"adversarial-self-supervised-learning-for-out","title":"Adversarial Self-Supervised Learning for Out-of-Domain Detection","date":"2021-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/about-explicit-variance-minimization-training","slug":"about-explicit-variance-minimization-training","title":"About Explicit Variance Minimization: Training Neural Networks for Medical Imaging With Limited Data Annotations","date":"2021-05-28","arxiv_id":"2105.14117","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-bug-detection-and-repair","slug":"self-supervised-bug-detection-and-repair","title":"Self-Supervised Bug Detection and Repair","date":"2021-05-26","arxiv_id":"2105.12787","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/self-supervised-bug-detection-and-repair#ran","syntology_url":"https://syntology.ai/paper/2105.12787","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.12787"}},"official":{"repos":["microsoft/neurips21-self-supervised-bug-detection-and-repair"],"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/sli2vol-annotate-a-3d-volume-from-a-single","slug":"sli2vol-annotate-a-3d-volume-from-a-single","title":"Sli2Vol: Annotate a 3D Volume from a Single Slice with Self-Supervised Learning","date":"2021-05-26","arxiv_id":"2105.12722","repositories_listed":1,"syntology":null},{"url":"/paper/graph-self-supervised-learning-the-bt-the","slug":"graph-self-supervised-learning-the-bt-the","title":"GraphVICRegHSIC: Towards improved self-supervised representation learning for graphs with a hyrbid loss function","date":"2021-05-25","arxiv_id":"2105.12247","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-graph-representation-learning-2","slug":"self-supervised-graph-representation-learning-2","title":"Self-Supervised Graph Representation Learning via Topology Transformations","date":"2021-05-25","arxiv_id":"2105.11689","repositories_listed":1,"syntology":null},{"url":"/paper/a-self-supervised-learning-strategy-for","slug":"a-self-supervised-learning-strategy-for","title":"A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections","date":"2021-05-24","arxiv_id":"2105.11239","repositories_listed":1,"syntology":null}],"record_sha256":"85e17b273314faa4b4dc755752581e5b96e02a8d29202b2650bffa91ce477d10","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}