{"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":"/method/jigsaw/papers/2","list_of":"/method/jigsaw","method":"Jigsaw","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":2,"pages_in_order":2,"rows_per_page":100,"rows":[101,140],"of":140,"counts":{"archive_papers_tagged":140,"with_a_code_link":55,"where_syntology_ran_a_sample":13,"not_listed_spam_title":0,"listed":140,"listed_where_code_ran":13,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":13,"every_run_a_failure_of_syntologys_instrument":0,"listed_with_a_run_with_no_instrument_failure":13,"listed_every_run_a_failure_of_syntologys_instrument":0,"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":"/method/jigsaw","prev":"/method/jigsaw","next":null,"papers":[{"paper":"/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","n_code_links":1,"syntology":null},{"paper":null,"slug":"improving-colonoscopy-lesion-classification","title":"Improving colonoscopy lesion classification using semi-supervised deep learning","date":"2020-09-07","arxiv_id":"2009.03162","n_code_links":0,"syntology":null},{"paper":"/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","n_code_links":1,"syntology":null},{"paper":null,"slug":"lazy-caterer-jigsaw-puzzles-models-properties","title":"Pictorial and apictorial polygonal jigsaw puzzles: The lazy caterer model, properties, and solvers","date":"2020-08-17","arxiv_id":"2008.07644","n_code_links":0,"syntology":null},{"paper":null,"slug":"demystifying-contrastive-self-supervised","title":"Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases","date":"2020-07-28","arxiv_id":"2007.13916","n_code_links":0,"syntology":null},{"paper":null,"slug":"reading-between-the-demographic-lines","title":"Reading Between the Demographic Lines: Resolving Sources of Bias in Toxicity Classifiers","date":"2020-06-29","arxiv_id":"2006.16402","n_code_links":0,"syntology":null},{"paper":null,"slug":"systematic-attack-surface-reduction-for","title":"Systematic Attack Surface Reduction For Deployed Sentiment Analysis Models","date":"2020-06-19","arxiv_id":"2006.11130","n_code_links":0,"syntology":null},{"paper":"/paper/3d-self-supervised-methods-for-medical","slug":"3d-self-supervised-methods-for-medical","title":"3D Self-Supervised Methods for Medical Imaging","date":"2020-06-06","arxiv_id":"2006.03829","n_code_links":1,"syntology":{"ran":14,"of":16,"n_ran_checked":14,"n_instrument":0,"unverified":2,"pointer_only":16,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["HealthML/self-supervised-3d-tasks"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"solving-mixed-modal-jigsaw-puzzle-for-fine","title":"Solving Mixed-Modal Jigsaw Puzzle for Fine-Grained Sketch-Based Image Retrieval","date":"2020-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"deepzzle-solving-visual-jigsaw-puzzles-with","title":"Deepzzle: Solving Visual Jigsaw Puzzles with Deep Learning andShortest Path Optimization","date":"2020-05-26","arxiv_id":"2005.12548","n_code_links":0,"syntology":null},{"paper":null,"slug":"domain-adaptive-relational-reasoning-for-3d","title":"Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation","date":"2020-05-18","arxiv_id":"2005.09120","n_code_links":0,"syntology":null},{"paper":null,"slug":"jigsaw-a-tool-for-discovering-explanatory","title":"JigSaw: A tool for discovering explanatory high-order interactions from random forests","date":"2020-05-09","arxiv_id":"2005.04342","n_code_links":0,"syntology":null},{"paper":null,"slug":"line-a-line-a-tool-for-annotating-word","title":"Line-a-line: A Tool for Annotating Word-Alignments","date":"2020-05-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/gradient-induced-co-saliency-detection","slug":"gradient-induced-co-saliency-detection","title":"Gradient-Induced Co-Saliency Detection","date":"2020-04-28","arxiv_id":"2004.13364","n_code_links":1,"syntology":null},{"paper":"/paper/extending-and-analyzing-self-supervised","slug":"extending-and-analyzing-self-supervised","title":"Extending and Analyzing Self-Supervised Learning Across Domains","date":"2020-04-24","arxiv_id":"2004.11992","n_code_links":1,"syntology":null},{"paper":"/paper/fine-grained-visual-classification-via","slug":"fine-grained-visual-classification-via","title":"Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches","date":"2020-03-08","arxiv_id":"2003.03836","n_code_links":5,"syntology":{"ran":3,"of":6,"n_ran_checked":1,"n_instrument":2,"unverified":3,"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","official":{"repos":["PRIS-CV/PMG-Progressive-Multi-Granularity-Training","RuoyiDu/PMG-Progressive-Multi-Granularity-Training"],"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"]}}},{"paper":null,"slug":"a-novel-hybrid-scheme-using-genetic","title":"A Novel Hybrid Scheme Using Genetic Algorithms and Deep Learning for the Reconstruction of Portuguese Tile Panels","date":"2019-12-04","arxiv_id":"1912.02707","n_code_links":0,"syntology":null},{"paper":"/paper/self-supervised-learning-of-pretext-invariant","slug":"self-supervised-learning-of-pretext-invariant","title":"Self-Supervised Learning of Pretext-Invariant Representations","date":"2019-12-04","arxiv_id":"1912.01991","n_code_links":7,"syntology":{"ran":3,"of":4,"n_ran_checked":3,"n_instrument":0,"unverified":1,"pointer_only":4,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","official":null}},{"paper":null,"slug":"superpixel-soup-monocular-dense-3d","title":"Superpixel Soup: Monocular Dense 3D Reconstruction of a Complex Dynamic Scene","date":"2019-11-19","arxiv_id":"1911.09092","n_code_links":0,"syntology":null},{"paper":"/paper/towards-shape-biased-unsupervised","slug":"towards-shape-biased-unsupervised","title":"Towards Shape Biased Unsupervised Representation Learning for Domain Generalization","date":"2019-09-18","arxiv_id":"1909.08245","n_code_links":0,"syntology":null},{"paper":null,"slug":"tackling-partial-domain-adaptation-with-self","title":"Tackling Partial Domain Adaptation with Self-Supervision","date":"2019-06-12","arxiv_id":"1906.05199","n_code_links":0,"syntology":null},{"paper":"/paper/domain-generalization-by-solving-jigsaw-1","slug":"domain-generalization-by-solving-jigsaw-1","title":"Domain Generalization by Solving Jigsaw Puzzles","date":"2019-06-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/domain-generalization-by-solving-jigsaw","slug":"domain-generalization-by-solving-jigsaw","title":"Domain Generalization by Solving Jigsaw Puzzles","date":"2019-03-16","arxiv_id":"1903.06864","n_code_links":2,"syntology":null},{"paper":null,"slug":"a-machine-learning-approach-to-comment","title":"A Machine Learning Approach to Comment Toxicity Classification","date":"2019-02-27","arxiv_id":"1903.06765","n_code_links":0,"syntology":null},{"paper":"/paper/iterative-reorganization-with-weak-spatial","slug":"iterative-reorganization-with-weak-spatial","title":"Iterative Reorganization with Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning","date":"2018-12-02","arxiv_id":"1812.00329","n_code_links":1,"syntology":null},{"paper":null,"slug":"solving-pictorial-jigsaw-puzzle-by-stigmergy","title":"Solving Pictorial Jigsaw Puzzle by Stigmergy-inspired Internet-based Human Collective Intelligence","date":"2018-11-28","arxiv_id":"1812.02559","n_code_links":0,"syntology":null},{"paper":"/paper/solving-jigsaw-puzzles-by-the-graph","slug":"solving-jigsaw-puzzles-by-the-graph","title":"Solving Jigsaw Puzzles By the Graph Connection Laplacian","date":"2018-11-07","arxiv_id":"1811.03188","n_code_links":3,"syntology":{"ran":16,"of":21,"n_ran_checked":12,"n_instrument":4,"unverified":5,"pointer_only":0,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 1 honoured, 0 violated, 11 with no contract checked; 4 where Syntology's instrument failed) · 5 unverified","official":{"repos":["vahanhuroyan/PuzzleDemoGCL"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"machine-learning-suites-for-online-toxicity","title":"Machine Learning Suites for Online Toxicity Detection","date":"2018-10-03","arxiv_id":"1810.01869","n_code_links":0,"syntology":null},{"paper":null,"slug":"video-jigsaw-unsupervised-learning-of","title":"Video Jigsaw: Unsupervised Learning of Spatiotemporal Context for Video Action Recognition","date":"2018-08-22","arxiv_id":"1808.07507","n_code_links":0,"syntology":null},{"paper":null,"slug":"jigsaw-puzzle-solving-using-local-feature-co","title":"Jigsaw Puzzle Solving Using Local Feature Co-Occurrences in Deep Neural Networks","date":"2018-07-05","arxiv_id":"1807.03155","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-image-representations-by-completing","title":"Learning Image Representations by Completing Damaged Jigsaw Puzzles","date":"2018-02-06","arxiv_id":"1802.01880","n_code_links":0,"syntology":null},{"paper":null,"slug":"dnn-buddies-a-deep-neural-network-based","title":"DNN-Buddies: A Deep Neural Network-Based Estimation Metric for the Jigsaw Puzzle Problem","date":"2017-11-23","arxiv_id":"1711.08762","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-generalized-genetic-algorithm-based-solver","title":"A Generalized Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles of Complex Types","date":"2017-11-17","arxiv_id":"1711.06768","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-genetic-algorithm-based-solver-for-very","title":"A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles","date":"2017-11-17","arxiv_id":"1711.06769","n_code_links":0,"syntology":null},{"paper":null,"slug":"from-square-pieces-to-brick-walls-the-next","title":"From Square Pieces to Brick Walls: The Next Challenge in Solving Jigsaw Puzzles","date":"2017-10-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"generating-music-medleys-via-playing-music","title":"Generating Music Medleys via Playing Music Puzzle Games","date":"2017-09-13","arxiv_id":"1709.04384","n_code_links":0,"syntology":null},{"paper":null,"slug":"monocular-dense-3d-reconstruction-of-a","title":"Monocular Dense 3D Reconstruction of a Complex Dynamic Scene from Two Perspective Frames","date":"2017-08-15","arxiv_id":"1708.04398","n_code_links":0,"syntology":null},{"paper":null,"slug":"deceiving-googles-perspective-api-built-for","title":"Deceiving Google's Perspective API Built for Detecting Toxic Comments","date":"2017-02-27","arxiv_id":"1702.08138","n_code_links":0,"syntology":null},{"paper":null,"slug":"solving-small-piece-jigsaw-puzzles-by-growing","title":"Solving Small-Piece Jigsaw Puzzles by Growing Consensus","date":"2016-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/unsupervised-learning-of-visual-1","slug":"unsupervised-learning-of-visual-1","title":"Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles","date":"2016-03-30","arxiv_id":"1603.09246","n_code_links":9,"syntology":{"ran":10,"of":13,"n_ran_checked":7,"n_instrument":3,"unverified":3,"pointer_only":7,"phrase":"10 ran (of which 6 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","official":null}}],"record_sha256":"b92398ae0f6daa724abbe7e8f9606d16472043e34855a445623358f40a348bdf","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}