{"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/22","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":22,"pages_in_order":51,"rows_per_page":100,"rows":[2101,2200],"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/21","next":"/task/self-supervised-learning/papers/23","papers":[{"url":"/paper/concept-generalization-in-visual","slug":"concept-generalization-in-visual","title":"Concept Generalization in Visual Representation Learning","date":"2020-12-10","arxiv_id":"2012.05649","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-transformation-for-self","slug":"contrastive-transformation-for-self","title":"Contrastive Transformation for Self-supervised Correspondence Learning","date":"2020-12-09","arxiv_id":"2012.05057","repositories_listed":1,"syntology":null},{"url":"/paper/pre-training-protein-language-models-with","slug":"pre-training-protein-language-models-with","title":"Pre-training Protein Language Models with Label-Agnostic Binding Pairs Enhances Performance in Downstream Tasks","date":"2020-12-05","arxiv_id":"2012.03084","repositories_listed":1,"syntology":null},{"url":"/paper/is-it-a-plausible-colour-ucapsnet-for-image","slug":"is-it-a-plausible-colour-ucapsnet-for-image","title":"Is It a Plausible Colour? UCapsNet for Image Colourisation","date":"2020-12-04","arxiv_id":"2012.02478","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-of-pixel-wise","slug":"self-supervised-learning-of-pixel-wise","title":"SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images","date":"2020-12-04","arxiv_id":"2012.02383","repositories_listed":1,"syntology":null},{"url":"/paper/co-mining-self-supervised-learning-for","slug":"co-mining-self-supervised-learning-for","title":"Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection","date":"2020-12-03","arxiv_id":"2012.01950","repositories_listed":1,"syntology":null},{"url":"/paper/using-cross-loss-influence-functions-to","slug":"using-cross-loss-influence-functions-to","title":"Cross-Loss Influence Functions to Explain Deep Network Representations","date":"2020-12-03","arxiv_id":"2012.01685","repositories_listed":1,"syntology":null},{"url":"/paper/patch2self-denoising-diffusion-mri-with-self-1","slug":"patch2self-denoising-diffusion-mri-with-self-1","title":"Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning​","date":"2020-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/scaling-down-deep-learning","slug":"scaling-down-deep-learning","title":"Scaling Down Deep Learning with MNIST-1D","date":"2020-11-29","arxiv_id":"2011.14439","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-time-series-representation-1","slug":"self-supervised-time-series-representation-1","title":"Self-Supervised Time Series Representation Learning by Inter-Intra Relational Reasoning","date":"2020-11-27","arxiv_id":"2011.13548","repositories_listed":1,"syntology":null},{"url":"/paper/task-programming-learning-data-efficient","slug":"task-programming-learning-data-efficient","title":"Task Programming: Learning Data Efficient Behavior Representations","date":"2020-11-27","arxiv_id":"2011.13917","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/task-programming-learning-data-efficient#ran","syntology_url":"https://syntology.ai/paper/2011.13917","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.13917"}},"official":{"repos":["neuroethology/TREBA"],"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/how-well-do-self-supervised-models-transfer","slug":"how-well-do-self-supervised-models-transfer","title":"How Well Do Self-Supervised Models Transfer?","date":"2020-11-26","arxiv_id":"2011.13377","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"4 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; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/how-well-do-self-supervised-models-transfer#ran","syntology_url":"https://syntology.ai/paper/2011.13377","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.13377"}},"official":{"repos":["linusericsson/ssl-transfer"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-to-infer-shape-programs-using-latent","slug":"learning-to-infer-shape-programs-using-latent","title":"PLAD: Learning to Infer Shape Programs with Pseudo-Labels and Approximate Distributions","date":"2020-11-25","arxiv_id":"2011.13045","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/learning-to-infer-shape-programs-using-latent#ran","syntology_url":"https://syntology.ai/paper/2011.13045","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.13045"}},"official":{"repos":["rkjones4/plad"],"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/dissecting-image-crops","slug":"dissecting-image-crops","title":"Dissecting Image Crops","date":"2020-11-24","arxiv_id":"2011.11831","repositories_listed":1,"syntology":null},{"url":"/paper/boosting-contrastive-self-supervised-learning","slug":"boosting-contrastive-self-supervised-learning","title":"Boosting Contrastive Self-Supervised Learning with False Negative Cancellation","date":"2020-11-23","arxiv_id":"2011.11765","repositories_listed":1,"syntology":null},{"url":"/paper/geography-aware-self-supervised-learning","slug":"geography-aware-self-supervised-learning","title":"Geography-Aware Self-Supervised Learning","date":"2020-11-19","arxiv_id":"2011.09980","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/geography-aware-self-supervised-learning#ran","syntology_url":"https://syntology.ai/paper/2011.09980","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.09980"}},"official":{"repos":["sustainlab-group/geography-aware-ssl"],"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/node-similarity-preserving-graph","slug":"node-similarity-preserving-graph","title":"Node Similarity Preserving Graph Convolutional Networks","date":"2020-11-19","arxiv_id":"2011.09643","repositories_listed":1,"syntology":null},{"url":"/paper/watch-and-learn-mapping-language-and-noisy","slug":"watch-and-learn-mapping-language-and-noisy","title":"Watch and Learn: Mapping Language and Noisy Real-world Videos with Self-supervision","date":"2020-11-19","arxiv_id":"2011.09634","repositories_listed":1,"syntology":null},{"url":"/paper/anomaly-detection-in-video-via-self","slug":"anomaly-detection-in-video-via-self","title":"Anomaly Detection in Video via Self-Supervised and Multi-Task Learning","date":"2020-11-15","arxiv_id":"2011.07491","repositories_listed":1,"syntology":null},{"url":"/paper/actbert-learning-global-local-video-text-1","slug":"actbert-learning-global-local-video-text-1","title":"ActBERT: Learning Global-Local Video-Text Representations","date":"2020-11-14","arxiv_id":"2011.07231","repositories_listed":1,"syntology":null},{"url":"/paper/a-self-supervised-learning-system-for-object-1","slug":"a-self-supervised-learning-system-for-object-1","title":"A Self-supervised Learning System for Object Detection in Videos Using Random Walks on Graphs","date":"2020-11-10","arxiv_id":"2011.05459","repositories_listed":1,"syntology":null},{"url":"/paper/magneto-an-efficient-deep-learning-method-for-1","slug":"magneto-an-efficient-deep-learning-method-for-1","title":"MAGNeto: An Efficient Deep Learning Method for the Extractive Tags Summarization Problem","date":"2020-11-09","arxiv_id":"2011.04349","repositories_listed":1,"syntology":null},{"url":"/paper/learning-a-geometric-representation-for-data","slug":"learning-a-geometric-representation-for-data","title":"Learning a Geometric Representation for Data-Efficient Depth Estimation via Gradient Field and Contrastive Loss","date":"2020-11-06","arxiv_id":"2011.03207","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-from-contrastive","slug":"self-supervised-learning-from-contrastive","title":"Self-Supervised Learning from Contrastive Mixtures for Personalized Speech Enhancement","date":"2020-11-06","arxiv_id":"2011.03426","repositories_listed":1,"syntology":null},{"url":"/paper/learning-visual-representations-for-transfer-1","slug":"learning-visual-representations-for-transfer-1","title":"Learning Visual Representations for Transfer Learning by Suppressing Texture","date":"2020-11-03","arxiv_id":"2011.01901","repositories_listed":1,"syntology":null},{"url":"/paper/patch2self-denoising-diffusion-mri-with-self","slug":"patch2self-denoising-diffusion-mri-with-self","title":"Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning","date":"2020-11-02","arxiv_id":"2011.01355","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/patch2self-denoising-diffusion-mri-with-self#ran","syntology_url":"https://syntology.ai/paper/2011.01355","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.01355"}},"official":{"repos":["ShreyasFadnavis/patch2self"],"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-representation-learning-for-3","slug":"self-supervised-representation-learning-for-3","title":"Self-supervised Representation Learning for Evolutionary Neural Architecture Search","date":"2020-10-31","arxiv_id":"2011.00186","repositories_listed":1,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 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; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/self-supervised-representation-learning-for-3#ran","syntology_url":"https://syntology.ai/paper/2011.00186","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2011.00186"}},"official":{"repos":["auroua/SSNENAS"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/joint-masked-cpc-and-ctc-training-for-asr","slug":"joint-masked-cpc-and-ctc-training-for-asr","title":"Joint Masked CPC and CTC Training for ASR","date":"2020-10-30","arxiv_id":"2011.00093","repositories_listed":1,"syntology":null},{"url":"/paper/combining-self-training-and-self-supervised","slug":"combining-self-training-and-self-supervised","title":"Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection","date":"2020-10-29","arxiv_id":"2010.15360","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-video-representation-using","slug":"self-supervised-video-representation-using","title":"Pretext-Contrastive Learning: Toward Good Practices in Self-supervised Video Representation Leaning","date":"2020-10-29","arxiv_id":"2010.15464","repositories_listed":1,"syntology":null},{"url":"/paper/multimodal-emotion-recognition-with","slug":"multimodal-emotion-recognition-with","title":"Multimodal Emotion Recognition with Transformer-Based Self Supervised Feature Fusion","date":"2020-10-27","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/pre-training-text-to-text-transformers-for","slug":"pre-training-text-to-text-transformers-for","title":"Pre-training Text-to-Text Transformers for Concept-centric Common Sense","date":"2020-10-24","arxiv_id":"2011.07956","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-self-supervised-learning-for-2","slug":"contrastive-self-supervised-learning-for-2","title":"Contrastive Self-Supervised Learning for Wireless Power Control","date":"2020-10-22","arxiv_id":"2010.11909","repositories_listed":1,"syntology":null},{"url":"/paper/self-alignment-pre-training-for-biomedical","slug":"self-alignment-pre-training-for-biomedical","title":"Self-Alignment Pretraining for Biomedical Entity Representations","date":"2020-10-22","arxiv_id":"2010.11784","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-of-part-mobility","slug":"self-supervised-learning-of-part-mobility","title":"Self-Supervised Learning of Part Mobility from Point Cloud Sequence","date":"2020-10-20","arxiv_id":"2010.11735","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-visual-attention-learning-for","slug":"self-supervised-visual-attention-learning-for","title":"Self-supervised Geometric Features Discovery via Interpretable Attention for Vehicle Re-Identification and Beyond","date":"2020-10-19","arxiv_id":"2010.09221","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-youtube-communities-via","slug":"understanding-youtube-communities-via","title":"Understanding YouTube Communities via Subscription-based Channel Embeddings","date":"2020-10-19","arxiv_id":"2010.09892","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-natural-language-inference-via","slug":"unsupervised-natural-language-inference-via","title":"Unsupervised Natural Language Inference via Decoupled Multimodal Contrastive Learning","date":"2020-10-16","arxiv_id":"2010.08200","repositories_listed":1,"syntology":null},{"url":"/paper/mixco-mix-up-contrastive-learning-for-visual","slug":"mixco-mix-up-contrastive-learning-for-visual","title":"MixCo: Mix-up Contrastive Learning for Visual Representation","date":"2020-10-13","arxiv_id":"2010.06300","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":1,"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 1 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/mixco-mix-up-contrastive-learning-for-visual#ran","syntology_url":"https://syntology.ai/paper/2010.06300","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.06300"}},"official":{"repos":["Lee-Gihun/MixCo-Mixup-Contrast"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/ms-2-l-multi-task-self-supervised-learning","slug":"ms-2-l-multi-task-self-supervised-learning","title":"MS$^2$L: Multi-Task Self-Supervised Learning for Skeleton Based Action Recognition","date":"2020-10-12","arxiv_id":"2010.05599","repositories_listed":1,"syntology":null},{"url":"/paper/guiding-attention-for-self-supervised","slug":"guiding-attention-for-self-supervised","title":"Guiding Attention for Self-Supervised Learning with Transformers","date":"2020-10-06","arxiv_id":"2010.02399","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/guiding-attention-for-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2010.02399","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.02399"}},"official":{"repos":["ameet-1997/AttentionGuidance"],"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/eqco-equivalent-rules-for-self-supervised-1","slug":"eqco-equivalent-rules-for-self-supervised-1","title":"EqCo: Equivalent Rules for Self-supervised Contrastive Learning","date":"2020-10-05","arxiv_id":"2010.01929","repositories_listed":1,"syntology":null},{"url":"/paper/hard-negative-mixing-for-contrastive-learning","slug":"hard-negative-mixing-for-contrastive-learning","title":"Hard Negative Mixing for Contrastive Learning","date":"2020-10-02","arxiv_id":"2010.01028","repositories_listed":1,"syntology":null},{"url":"/paper/xda-accurate-robust-disassembly-with-transfer","slug":"xda-accurate-robust-disassembly-with-transfer","title":"XDA: Accurate, Robust Disassembly with Transfer Learning","date":"2020-10-02","arxiv_id":"2010.00770","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/xda-accurate-robust-disassembly-with-transfer#ran","syntology_url":"https://syntology.ai/paper/2010.00770","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.00770"}},"official":{"repos":["CUMLSec/XDA"],"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/self-supervised-few-shot-learning-on-point","slug":"self-supervised-few-shot-learning-on-point","title":"Self-Supervised Few-Shot Learning on Point Clouds","date":"2020-09-29","arxiv_id":"2009.14168","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":3,"n_ran_checked":3,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"4 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/self-supervised-few-shot-learning-on-point#ran","syntology_url":"https://syntology.ai/paper/2009.14168","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2009.14168"}},"official":null}},{"url":"/paper/g-simclr-self-supervised-contrastive-learning","slug":"g-simclr-self-supervised-contrastive-learning","title":"G-SimCLR: Self-Supervised Contrastive Learning with Guided Projection via Pseudo Labelling","date":"2020-09-28","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/an-unsupervised-sentence-embedding-method","slug":"an-unsupervised-sentence-embedding-method","title":"An Unsupervised Sentence Embedding Method by Mutual Information Maximization","date":"2020-09-25","arxiv_id":"2009.12061","repositories_listed":1,"syntology":null},{"url":"/paper/neural-identification-for-control","slug":"neural-identification-for-control","title":"Neural Identification for Control","date":"2020-09-24","arxiv_id":"2009.11782","repositories_listed":1,"syntology":null},{"url":"/paper/online-semi-supervised-learning-in-contextual","slug":"online-semi-supervised-learning-in-contextual","title":"Online Semi-Supervised Learning in Contextual Bandits with Episodic Reward","date":"2020-09-17","arxiv_id":"2009.08457","repositories_listed":1,"syntology":null},{"url":"/paper/boosting-generalization-in-bio-signal","slug":"boosting-generalization-in-bio-signal","title":"Boosting Generalization in Bio-Signal Classification by Learning the Phase-Amplitude Coupling","date":"2020-09-16","arxiv_id":"2009.07664","repositories_listed":1,"syntology":null},{"url":"/paper/evaluating-self-supervised-pretraining","slug":"evaluating-self-supervised-pretraining","title":"SelfAugment: Automatic Augmentation Policies for Self-Supervised Learning","date":"2020-09-16","arxiv_id":"2009.07724","repositories_listed":1,"syntology":null},{"url":"/paper/task-specific-objectives-of-pre-trained","slug":"task-specific-objectives-of-pre-trained","title":"Dialogue-adaptive Language Model Pre-training From Quality Estimation","date":"2020-09-10","arxiv_id":"2009.04984","repositories_listed":1,"syntology":null},{"url":"/paper/a-self-supervised-gait-encoding-approach-with","slug":"a-self-supervised-gait-encoding-approach-with","title":"A Self-Supervised Gait Encoding Approach with Locality-Awareness for 3D Skeleton Based Person Re-Identification","date":"2020-09-05","arxiv_id":"2009.03671","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-based-on-spatial","slug":"self-supervised-learning-based-on-spatial","title":"Self-Supervised Learning Based on Spatial Awareness for Medical Image Analysis","date":"2020-08-30","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/puzzle-ae-novelty-detection-in-images-through","slug":"puzzle-ae-novelty-detection-in-images-through","title":"Puzzle-AE: Novelty Detection in Images through Solving Puzzles","date":"2020-08-29","arxiv_id":"2008.12959","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-for-large-scale","slug":"self-supervised-learning-for-large-scale","title":"Self-Supervised Learning for Large-Scale Unsupervised Image Clustering","date":"2020-08-24","arxiv_id":"2008.10312","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/self-supervised-learning-for-large-scale#ran","syntology_url":"https://syntology.ai/paper/2008.10312","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.10312"}},"official":{"repos":["Randl/kmeans_selfsuper"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/neutral-face-game-character-auto-creation-via","slug":"neutral-face-game-character-auto-creation-via","title":"Neutral Face Game Character Auto-Creation via PokerFace-GAN","date":"2020-08-17","arxiv_id":"2008.07154","repositories_listed":1,"syntology":null},{"url":"/paper/reversing-the-cycle-self-supervised-deep","slug":"reversing-the-cycle-self-supervised-deep","title":"Reversing the cycle: self-supervised deep stereo through enhanced monocular distillation","date":"2020-08-17","arxiv_id":"2008.07130","repositories_listed":1,"syntology":{"n":15,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":10,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"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) · 10 unverified","sample_list":"/paper/reversing-the-cycle-self-supervised-deep#ran","syntology_url":"https://syntology.ai/paper/2008.07130","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.07130"}},"official":{"repos":["FilippoAleotti/Reversing"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":10,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-learning-for-monocular-depth","slug":"self-supervised-learning-for-monocular-depth","title":"Self-Supervised Learning for Monocular Depth Estimation from Aerial Imagery","date":"2020-08-17","arxiv_id":"2008.07246","repositories_listed":1,"syntology":null},{"url":"/paper/jointly-fine-tuning-bert-like-self-supervised-1","slug":"jointly-fine-tuning-bert-like-self-supervised-1","title":"Jointly Fine-Tuning “BERT-like” Self Supervised Models to Improve Multimodal Speech Emotion Recognition","date":"2020-08-15","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/neural-ray-surfaces-for-self-supervised","slug":"neural-ray-surfaces-for-self-supervised","title":"Neural Ray Surfaces for Self-Supervised Learning of Depth and Ego-motion","date":"2020-08-15","arxiv_id":"2008.06630","repositories_listed":1,"syntology":null},{"url":"/paper/self-adapting-confidence-estimation-for","slug":"self-adapting-confidence-estimation-for","title":"Self-adapting confidence estimation for stereo","date":"2020-08-14","arxiv_id":"2008.06447","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-of-audio-visual","slug":"self-supervised-learning-of-audio-visual","title":"Self-Supervised Learning of Audio-Visual Objects from Video","date":"2020-08-10","arxiv_id":"2008.04237","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/self-supervised-learning-of-audio-visual#ran","syntology_url":"https://syntology.ai/paper/2008.04237","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.04237"}},"official":null}},{"url":"/paper/functional-regularization-for-representation","slug":"functional-regularization-for-representation","title":"Functional Regularization for Representation Learning: A Unified Theoretical Perspective","date":"2020-08-06","arxiv_id":"2008.02447","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/functional-regularization-for-representation#ran","syntology_url":"https://syntology.ai/paper/2008.02447","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.02447"}},"official":{"repos":["sid7954/functional-regularization"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-learning-using-consistency","slug":"self-supervised-learning-using-consistency","title":"Self-supervised learning using consistency regularization of spatio-temporal data augmentation for action recognition","date":"2020-08-05","arxiv_id":"2008.02086","repositories_listed":1,"syntology":null},{"url":"/paper/memory-augmented-dense-predictive-coding-for","slug":"memory-augmented-dense-predictive-coding-for","title":"Memory-augmented Dense Predictive Coding for Video Representation Learning","date":"2020-08-03","arxiv_id":"2008.01065","repositories_listed":1,"syntology":{"n":13,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/memory-augmented-dense-predictive-coding-for#ran","syntology_url":"https://syntology.ai/paper/2008.01065","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.01065"}},"official":{"repos":["TengdaHan/MemDPC"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-object-tracking-with-cycle","slug":"self-supervised-object-tracking-with-cycle","title":"Self-supervised Object Tracking with Cycle-consistent Siamese Networks","date":"2020-08-03","arxiv_id":"2008.00637","repositories_listed":1,"syntology":null},{"url":"/paper/distilling-visual-priors-from-self-supervised","slug":"distilling-visual-priors-from-self-supervised","title":"Distilling Visual Priors from Self-Supervised Learning","date":"2020-08-01","arxiv_id":"2008.00261","repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-acoustic-images-for-effective-self","slug":"leveraging-acoustic-images-for-effective-self","title":"Leveraging Acoustic Images for Effective Self-Supervised Audio Representation Learning","date":"2020-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-of-point-clouds-via","slug":"self-supervised-learning-of-point-clouds-via","title":"Self-supervised Learning of Point Clouds via Orientation Estimation","date":"2020-08-01","arxiv_id":"2008.00305","repositories_listed":1,"syntology":null},{"url":"/paper/kovis-keypoint-based-visual-servoing-with","slug":"kovis-keypoint-based-visual-servoing-with","title":"KOVIS: Keypoint-based Visual Servoing with Zero-Shot Sim-to-Real Transfer for Robotics Manipulation","date":"2020-07-28","arxiv_id":"2007.13960","repositories_listed":1,"syntology":null},{"url":"/paper/learning-from-scale-invariant-examples-for","slug":"learning-from-scale-invariant-examples-for","title":"Learning from Scale-Invariant Examples for Domain Adaptation in Semantic Segmentation","date":"2020-07-28","arxiv_id":"2007.14449","repositories_listed":1,"syntology":null},{"url":"/paper/crowdsourced-3d-mapping-a-combined-multi-view","slug":"crowdsourced-3d-mapping-a-combined-multi-view","title":"Crowdsourced 3D Mapping: A Combined Multi-View Geometry and Self-Supervised Learning Approach","date":"2020-07-25","arxiv_id":"2007.12918","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-for-deep-models-in","slug":"self-supervised-learning-for-deep-models-in","title":"Self-supervised Learning for Large-scale Item Recommendations","date":"2020-07-25","arxiv_id":"2007.12865","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/self-supervised-learning-for-deep-models-in#ran","syntology_url":"https://syntology.ai/paper/2007.12865","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.12865"}},"official":null}},{"url":"/paper/self-supervised-monocular-3d-face","slug":"self-supervised-monocular-3d-face","title":"Self-Supervised Monocular 3D Face Reconstruction by Occlusion-Aware Multi-view Geometry Consistency","date":"2020-07-24","arxiv_id":"2007.12494","repositories_listed":1,"syntology":{"n":16,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/self-supervised-monocular-3d-face#ran","syntology_url":"https://syntology.ai/paper/2007.12494","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.12494"}},"official":{"repos":["jiaxiangshang/MGCNet"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/relab-reliable-label-bootstrapping-for-semi","slug":"relab-reliable-label-bootstrapping-for-semi","title":"Reliable Label Bootstrapping for Semi-Supervised Learning","date":"2020-07-23","arxiv_id":"2007.11866","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-and-partially-supervised-learning","slug":"weakly-and-partially-supervised-learning","title":"Weakly and Partially Supervised Learning Frameworks for Anomaly Detection","date":"2020-07-23","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-of-contextual","slug":"self-supervised-learning-of-contextual","title":"Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks","date":"2020-07-22","arxiv_id":"2007.11192","repositories_listed":1,"syntology":null},{"url":"/paper/feature-metric-loss-for-self-supervised","slug":"feature-metric-loss-for-self-supervised","title":"Feature-metric Loss for Self-supervised Learning of Depth and Egomotion","date":"2020-07-21","arxiv_id":"2007.10603","repositories_listed":1,"syntology":null},{"url":"/paper/slnspeech-solving-extended-speech-separation","slug":"slnspeech-solving-extended-speech-separation","title":"CSLNSpeech: solving extended speech separation problem with the help of Chinese sign language","date":"2020-07-21","arxiv_id":"2007.10629","repositories_listed":1,"syntology":null},{"url":"/paper/learning-high-level-policies-for-model","slug":"learning-high-level-policies-for-model","title":"Learning High-Level Policies for Model Predictive Control","date":"2020-07-20","arxiv_id":"2007.10284","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-auxiliary-learning-with-meta","slug":"self-supervised-auxiliary-learning-with-meta","title":"Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs","date":"2020-07-16","arxiv_id":"2007.08294","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-representation-learning-for-2","slug":"self-supervised-representation-learning-for-2","title":"Self-Supervised Representation Learning for Detection of ACL Tear Injury in Knee MR Videos","date":"2020-07-15","arxiv_id":"2007.07761","repositories_listed":1,"syntology":null},{"url":"/paper/data-efficient-reinforcement-learning-with-1","slug":"data-efficient-reinforcement-learning-with-1","title":"Data-Efficient Reinforcement Learning with Self-Predictive Representations","date":"2020-07-12","arxiv_id":"2007.05929","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/data-efficient-reinforcement-learning-with-1#ran","syntology_url":"https://syntology.ai/paper/2007.05929","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.05929"}},"official":{"repos":["mila-iqia/spr"],"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/learning-latent-stochastic-differential","slug":"learning-latent-stochastic-differential","title":"Identifying Latent Stochastic Differential Equations","date":"2020-07-12","arxiv_id":"2007.06075","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-drivable-area-and-road","slug":"self-supervised-drivable-area-and-road","title":"Self-Supervised Drivable Area and Road Anomaly Segmentation using RGB-D Data for Robotic Wheelchairs","date":"2020-07-12","arxiv_id":"2007.05950","repositories_listed":1,"syntology":null},{"url":"/paper/divide-and-rule-self-supervised-learning-for","slug":"divide-and-rule-self-supervised-learning-for","title":"Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer","date":"2020-07-07","arxiv_id":"2007.03292","repositories_listed":1,"syntology":null},{"url":"/paper/self-domain-adapted-network","slug":"self-domain-adapted-network","title":"Self domain adapted network","date":"2020-07-07","arxiv_id":"2007.03162","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-skull-reconstruction-in-brain","slug":"self-supervised-skull-reconstruction-in-brain","title":"Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy","date":"2020-07-07","arxiv_id":"2007.03817","repositories_listed":1,"syntology":null},{"url":"/paper/add-analytically-differentiable-dynamics-for","slug":"add-analytically-differentiable-dynamics-for","title":"ADD: Analytically Differentiable Dynamics for Multi-Body Systems with Frictional Contact","date":"2020-07-02","arxiv_id":"2007.00987","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/add-analytically-differentiable-dynamics-for#ran","syntology_url":"https://syntology.ai/paper/2007.00987","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.00987"}},"official":null}},{"url":"/paper/sacnn-self-attention-convolutional-neural","slug":"sacnn-self-attention-convolutional-neural","title":"SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising With Self-Supervised Perceptual Loss Network","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-cnn-based-pansharpening-guided","slug":"rethinking-cnn-based-pansharpening-guided","title":"Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs","date":"2020-06-30","arxiv_id":"2006.16644","repositories_listed":1,"syntology":null},{"url":"/paper/subject-aware-contrastive-learning-for","slug":"subject-aware-contrastive-learning-for","title":"Subject-Aware Contrastive Learning for Biosignals","date":"2020-06-30","arxiv_id":"2007.04871","repositories_listed":1,"syntology":null},{"url":"/paper/simulation-of-brain-resection-for-cavity","slug":"simulation-of-brain-resection-for-cavity","title":"Simulation of Brain Resection for Cavity Segmentation Using Self-Supervised and Semi-Supervised Learning","date":"2020-06-28","arxiv_id":"2006.15693","repositories_listed":1,"syntology":null},{"url":"/paper/region-of-interest-guided-supervoxel","slug":"region-of-interest-guided-supervoxel","title":"Region-of-interest guided Supervoxel Inpainting for Self-supervision","date":"2020-06-26","arxiv_id":"2006.15186","repositories_listed":1,"syntology":null},{"url":"/paper/space-time-correspondence-as-a-contrastive","slug":"space-time-correspondence-as-a-contrastive","title":"Space-Time Correspondence as a Contrastive Random Walk","date":"2020-06-25","arxiv_id":"2006.14613","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":10,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":3,"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) · 3 unverified","sample_list":"/paper/space-time-correspondence-as-a-contrastive#ran","syntology_url":"https://syntology.ai/paper/2006.14613","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.14613"}},"official":{"repos":["ajabri/videowalk"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/gaining-insight-into-sars-cov-2-infection-and","slug":"gaining-insight-into-sars-cov-2-infection-and","title":"Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural Networks","date":"2020-06-23","arxiv_id":"2006.12971","repositories_listed":1,"syntology":null},{"url":"/paper/the-color-out-of-space-learning-self","slug":"the-color-out-of-space-learning-self","title":"The color out of space: learning self-supervised representations for Earth Observation imagery","date":"2020-06-22","arxiv_id":"2006.12119","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-color-out-of-space-learning-self#ran","syntology_url":"https://syntology.ai/paper/2006.12119","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.12119"}},"official":{"repos":["stevinc/TheColorOutOfSpace"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/unsupervised-image-classification-for-deep","slug":"unsupervised-image-classification-for-deep","title":"Unsupervised Image Classification for Deep Representation Learning","date":"2020-06-20","arxiv_id":"2006.11480","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-learning-of-global-and-local","slug":"contrastive-learning-of-global-and-local","title":"Contrastive learning of global and local features for medical image segmentation with limited annotations","date":"2020-06-18","arxiv_id":"2006.10511","repositories_listed":1,"syntology":null}],"record_sha256":"f9aa77ce979049b5fd080f7ebe9c386328da0b635440f28f5e27cb7c04086d34","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}