{"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/data-augmentation/papers/29","list_of":"/task/data-augmentation","task":"Data Augmentation","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":29,"pages_in_order":84,"rows_per_page":100,"rows":[2801,2900],"of":8378,"counts":{"archive_papers_tagged":8378,"with_a_code_link":3225,"where_syntology_ran_a_sample":692,"not_listed_spam_title":0,"listed":8378,"listed_where_code_ran":692,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":567,"every_run_a_failure_of_syntologys_instrument":125,"listed_with_a_run_with_no_instrument_failure":567,"listed_every_run_a_failure_of_syntologys_instrument":125,"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/data-augmentation","prev":"/task/data-augmentation/papers/28","next":"/task/data-augmentation/papers/30","papers":[{"url":"/paper/citisen-a-deep-learning-based-speech-signal","slug":"citisen-a-deep-learning-based-speech-signal","title":"CITISEN: A Deep Learning-Based Speech Signal-Processing Mobile Application","date":"2020-08-21","arxiv_id":"2008.09264","repositories_listed":1,"syntology":null},{"url":"/paper/method-to-classify-skin-lesions-using","slug":"method-to-classify-skin-lesions-using","title":"Method to Classify Skin Lesions using Dermoscopic images","date":"2020-08-21","arxiv_id":"2008.09418","repositories_listed":1,"syntology":null},{"url":"/paper/visualsem-a-high-quality-knowledge-graph-for","slug":"visualsem-a-high-quality-knowledge-graph-for","title":"VisualSem: A High-quality Knowledge Graph for Vision and Language","date":"2020-08-20","arxiv_id":"2008.09150","repositories_listed":1,"syntology":null},{"url":"/paper/anchor-free-small-scale-multispectral","slug":"anchor-free-small-scale-multispectral","title":"Anchor-free Small-scale Multispectral Pedestrian Detection","date":"2020-08-19","arxiv_id":"2008.08418","repositories_listed":1,"syntology":null},{"url":"/paper/regularization-and-normalization-for","slug":"regularization-and-normalization-for","title":"A Systematic Survey of Regularization and Normalization in GANs","date":"2020-08-19","arxiv_id":"2008.08930","repositories_listed":1,"syntology":null},{"url":"/paper/but-fit-at-semeval-2020-task-4-multilingual","slug":"but-fit-at-semeval-2020-task-4-multilingual","title":"BUT-FIT at SemEval-2020 Task 4: Multilingual commonsense","date":"2020-08-17","arxiv_id":"2008.07259","repositories_listed":1,"syntology":null},{"url":"/paper/storir-stochastic-room-impulse-response","slug":"storir-stochastic-room-impulse-response","title":"StoRIR: Stochastic Room Impulse Response Generation for Audio Data Augmentation","date":"2020-08-17","arxiv_id":"2008.07231","repositories_listed":1,"syntology":null},{"url":"/paper/bowtie-networks-generative-modeling-for-joint","slug":"bowtie-networks-generative-modeling-for-joint","title":"Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis","date":"2020-08-16","arxiv_id":"2008.06981","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":4,"n_ran_checked":4,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":7,"phrase":"6 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/bowtie-networks-generative-modeling-for-joint#ran","syntology_url":"https://syntology.ai/paper/2008.06981","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.06981"}},"official":{"repos":["zpbao/bowtie_networks"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/model-patching-closing-the-subgroup","slug":"model-patching-closing-the-subgroup","title":"Model Patching: Closing the Subgroup Performance Gap with Data Augmentation","date":"2020-08-15","arxiv_id":"2008.06775","repositories_listed":1,"syntology":{"n":24,"n_ran":18,"n_constructed":0,"n_ran_checked":17,"n_instrument":1,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":17,"n_pointer_only":0,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/model-patching-closing-the-subgroup#ran","syntology_url":"https://syntology.ai/paper/2008.06775","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.06775"}},"official":{"repos":["HazyResearch/model-patching"],"state":"official (archive's flag): 18 ran","n_ran":18,"n_constructed":0,"n_ran_no_instrument_failure":17,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/adaptation-algorithms-for-speech-recognition","slug":"adaptation-algorithms-for-speech-recognition","title":"Adaptation Algorithms for Neural Network-Based Speech Recognition: An Overview","date":"2020-08-14","arxiv_id":"2008.06580","repositories_listed":1,"syntology":null},{"url":"/paper/pointmixup-augmentation-for-point-clouds","slug":"pointmixup-augmentation-for-point-clouds","title":"PointMixup: Augmentation for Point Clouds","date":"2020-08-14","arxiv_id":"2008.06374","repositories_listed":1,"syntology":null},{"url":"/paper/learning-temporally-invariant-and-localizable","slug":"learning-temporally-invariant-and-localizable","title":"Learning Temporally Invariant and Localizable Features via Data Augmentation for Video Recognition","date":"2020-08-13","arxiv_id":"2008.05721","repositories_listed":1,"syntology":null},{"url":"/paper/deep-robust-clustering-by-contrastive","slug":"deep-robust-clustering-by-contrastive","title":"Deep Robust Clustering by Contrastive Learning","date":"2020-08-07","arxiv_id":"2008.03030","repositories_listed":1,"syntology":null},{"url":"/paper/mixing-specific-data-augmentation-techniques","slug":"mixing-specific-data-augmentation-techniques","title":"Mixing-Specific Data Augmentation Techniques for Improved Blind Violin/Piano Source Separation","date":"2020-08-06","arxiv_id":"2008.02480","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":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mixing-specific-data-augmentation-techniques#ran","syntology_url":"https://syntology.ai/paper/2008.02480","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.02480"}},"official":{"repos":["SunnyCYC/aug4mss"],"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/hierarchical-amortized-training-for-memory","slug":"hierarchical-amortized-training-for-memory","title":"Hierarchical Amortized Training for Memory-efficient High Resolution 3D GAN","date":"2020-08-05","arxiv_id":"2008.01910","repositories_listed":1,"syntology":null},{"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/nlpdove-at-semeval-2020-task-12-improving","slug":"nlpdove-at-semeval-2020-task-12-improving","title":"NLPDove at SemEval-2020 Task 12: Improving Offensive Language Detection with Cross-lingual Transfer","date":"2020-08-04","arxiv_id":"2008.01354","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-semantic-data-augmentation-for","slug":"adversarial-semantic-data-augmentation-for","title":"Adversarial Semantic Data Augmentation for Human Pose Estimation","date":"2020-08-03","arxiv_id":"2008.00697","repositories_listed":1,"syntology":null},{"url":"/paper/an-empirical-survey-of-data-augmentation-for","slug":"an-empirical-survey-of-data-augmentation-for","title":"An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks","date":"2020-07-31","arxiv_id":"2007.15951","repositories_listed":1,"syntology":{"n":19,"n_ran":14,"n_constructed":0,"n_ran_checked":14,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":0,"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) · 5 unverified","sample_list":"/paper/an-empirical-survey-of-data-augmentation-for#ran","syntology_url":"https://syntology.ai/paper/2007.15951","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.15951"}},"official":{"repos":["uchidalab/time_series_augmentation"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/robust-retinal-vessel-segmentation-from-a","slug":"robust-retinal-vessel-segmentation-from-a","title":"Robust Retinal Vessel Segmentation from a Data Augmentation Perspective","date":"2020-07-31","arxiv_id":"2007.15883","repositories_listed":1,"syntology":null},{"url":"/paper/ecnu-sensemaker-at-semeval-2020-task-4","slug":"ecnu-sensemaker-at-semeval-2020-task-4","title":"ECNU-SenseMaker at SemEval-2020 Task 4: Leveraging Heterogeneous Knowledge Resources for Commonsense Validation and Explanation","date":"2020-07-28","arxiv_id":"2007.14200","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/normal-bundle-bootstrap","slug":"normal-bundle-bootstrap","title":"Normal-bundle Bootstrap","date":"2020-07-27","arxiv_id":"2007.13869","repositories_listed":1,"syntology":null},{"url":"/paper/part-aware-data-augmentation-for-3d-object","slug":"part-aware-data-augmentation-for-3d-object","title":"Part-Aware Data Augmentation for 3D Object Detection in Point Cloud","date":"2020-07-27","arxiv_id":"2007.13373","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"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; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/part-aware-data-augmentation-for-3d-object#ran","syntology_url":"https://syntology.ai/paper/2007.13373","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.13373"}},"official":{"repos":["sky77764/pa-aug.pytorch"],"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/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/cycnn-a-rotation-invariant-cnn-using-polar","slug":"cycnn-a-rotation-invariant-cnn-using-polar","title":"CyCNN: A Rotation Invariant CNN using Polar Mapping and Cylindrical Convolution Layers","date":"2020-07-21","arxiv_id":"2007.10588","repositories_listed":1,"syntology":null},{"url":"/paper/membership-inference-with-privately-augmented","slug":"membership-inference-with-privately-augmented","title":"How Does Data Augmentation Affect Privacy in Machine Learning?","date":"2020-07-21","arxiv_id":"2007.10567","repositories_listed":1,"syntology":null},{"url":"/paper/regularizing-deep-networks-with-semantic-data","slug":"regularizing-deep-networks-with-semantic-data","title":"Regularizing Deep Networks with Semantic Data Augmentation","date":"2020-07-21","arxiv_id":"2007.10538","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":1,"n_ran_checked":4,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"6 ran (of which 1 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/regularizing-deep-networks-with-semantic-data#ran","syntology_url":"https://syntology.ai/paper/2007.10538","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.10538"}},"official":{"repos":["blackfeather-wang/ISDA-for-Deep-Networks"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/semantic-equivalent-adversarial-data","slug":"semantic-equivalent-adversarial-data","title":"Semantic Equivalent Adversarial Data Augmentation for Visual Question Answering","date":"2020-07-19","arxiv_id":"2007.09592","repositories_listed":1,"syntology":null},{"url":"/paper/onlineaugment-online-data-augmentation-with","slug":"onlineaugment-online-data-augmentation-with","title":"OnlineAugment: Online Data Augmentation with Less Domain Knowledge","date":"2020-07-17","arxiv_id":"2007.09271","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":3,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/onlineaugment-online-data-augmentation-with#ran","syntology_url":"https://syntology.ai/paper/2007.09271","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.09271"}},"official":{"repos":["zhiqiangdon/online-augment"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/surface-normal-estimation-of-tilted-images","slug":"surface-normal-estimation-of-tilted-images","title":"Surface Normal Estimation of Tilted Images via Spatial Rectifier","date":"2020-07-17","arxiv_id":"2007.09264","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-quantification-and-deep-ensembles","slug":"uncertainty-quantification-and-deep-ensembles","title":"Uncertainty Quantification and Deep Ensembles","date":"2020-07-17","arxiv_id":"2007.08792","repositories_listed":1,"syntology":null},{"url":"/paper/device-robust-acoustic-scene-classification","slug":"device-robust-acoustic-scene-classification","title":"Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data Augmentation","date":"2020-07-16","arxiv_id":"2007.08389","repositories_listed":1,"syntology":null},{"url":"/paper/data-efficient-deep-learning-method-for-image","slug":"data-efficient-deep-learning-method-for-image","title":"Data-Efficient Deep Learning Method for Image Classification Using Data Augmentation, Focal Cosine Loss, and Ensemble","date":"2020-07-15","arxiv_id":"2007.07805","repositories_listed":1,"syntology":null},{"url":"/paper/tracking-passengers-and-baggage-items-using","slug":"tracking-passengers-and-baggage-items-using","title":"Tracking Passengers and Baggage Items using Multi-camera Systems at Security Checkpoints","date":"2020-07-15","arxiv_id":"2007.07924","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/an-asymptotically-optimal-multi-armed-bandit","slug":"an-asymptotically-optimal-multi-armed-bandit","title":"An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter Optimization","date":"2020-07-11","arxiv_id":"2007.05670","repositories_listed":1,"syntology":null},{"url":"/paper/variable-skipping-for-autoregressive-range","slug":"variable-skipping-for-autoregressive-range","title":"Variable Skipping for Autoregressive Range Density Estimation","date":"2020-07-10","arxiv_id":"2007.05572","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/variable-skipping-for-autoregressive-range#ran","syntology_url":"https://syntology.ai/paper/2007.05572","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.05572"}},"official":{"repos":["var-skip/var-skip"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/boundary-thickness-and-robustness-in-learning","slug":"boundary-thickness-and-robustness-in-learning","title":"Boundary thickness and robustness in learning models","date":"2020-07-09","arxiv_id":"2007.05086","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":1,"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: 0 honoured, 1 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/boundary-thickness-and-robustness-in-learning#ran","syntology_url":"https://syntology.ai/paper/2007.05086","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.05086"}},"official":{"repos":["nsfzyzz/boundary_thickness"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/contrastive-code-representation-learning","slug":"contrastive-code-representation-learning","title":"Contrastive Code Representation Learning","date":"2020-07-09","arxiv_id":"2007.04973","repositories_listed":1,"syntology":null},{"url":"/paper/epi-based-oriented-relation-networks-for","slug":"epi-based-oriented-relation-networks-for","title":"EPI-based Oriented Relation Networks for Light Field Depth Estimation","date":"2020-07-09","arxiv_id":"2007.04538","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-task-driven-data-augmentation","slug":"semi-supervised-task-driven-data-augmentation","title":"Semi-supervised Task-driven Data Augmentation for Medical Image Segmentation","date":"2020-07-09","arxiv_id":"2007.05363","repositories_listed":1,"syntology":null},{"url":"/paper/stypath-style-transfer-data-augmentation-for","slug":"stypath-style-transfer-data-augmentation-for","title":"StyPath: Style-Transfer Data Augmentation For Robust Histology Image Classification","date":"2020-07-09","arxiv_id":"2007.05008","repositories_listed":1,"syntology":null},{"url":"/paper/when-perspective-comes-for-free-improving","slug":"when-perspective-comes-for-free-improving","title":"Camera Pose Matters: Improving Depth Prediction by Mitigating Pose Distribution Bias","date":"2020-07-08","arxiv_id":"2007.03887","repositories_listed":1,"syntology":null},{"url":"/paper/counterfactual-data-augmentation-using","slug":"counterfactual-data-augmentation-using","title":"Counterfactual Data Augmentation using Locally Factored Dynamics","date":"2020-07-06","arxiv_id":"2007.02863","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":0,"n_instrument":6,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"6 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; 6 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/counterfactual-data-augmentation-using#ran","syntology_url":"https://syntology.ai/paper/2007.02863","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.02863"}},"official":null}},{"url":"/paper/scaling-imitation-learning-in-minecraft","slug":"scaling-imitation-learning-in-minecraft","title":"Scaling Imitation Learning in Minecraft","date":"2020-07-06","arxiv_id":"2007.02701","repositories_listed":1,"syntology":null},{"url":"/paper/anatomical-data-augmentation-via-fluid-based","slug":"anatomical-data-augmentation-via-fluid-based","title":"Anatomical Data Augmentation via Fluid-based Image Registration","date":"2020-07-05","arxiv_id":"2007.02447","repositories_listed":1,"syntology":null},{"url":"/paper/pointtrack-for-effective-online-multi-object","slug":"pointtrack-for-effective-online-multi-object","title":"PointTrack++ for Effective Online Multi-Object Tracking and Segmentation","date":"2020-07-03","arxiv_id":"2007.01549","repositories_listed":1,"syntology":null},{"url":"/paper/can-we-achieve-more-with-less-exploring-data","slug":"can-we-achieve-more-with-less-exploring-data","title":"Can We Achieve More with Less? Exploring Data Augmentation for Toxic Comment Classification","date":"2020-07-02","arxiv_id":"2007.00875","repositories_listed":1,"syntology":null},{"url":"/paper/data-augmenting-contrastive-learning-of","slug":"data-augmenting-contrastive-learning-of","title":"Data Augmenting Contrastive Learning of Speech Representations in the Time Domain","date":"2020-07-02","arxiv_id":"2007.00991","repositories_listed":1,"syntology":null},{"url":"/paper/corefqa-coreference-resolution-as-query-based","slug":"corefqa-coreference-resolution-as-query-based","title":"CorefQA: Coreference Resolution as Query-based Span Prediction","date":"2020-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/bitmix-data-augmentation-for-image","slug":"bitmix-data-augmentation-for-image","title":"BitMix: Data Augmentation for Image Steganalysis","date":"2020-06-30","arxiv_id":"2006.16625","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/the-many-faces-of-robustness-a-critical","slug":"the-many-faces-of-robustness-a-critical","title":"The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization","date":"2020-06-29","arxiv_id":"2006.16241","repositories_listed":1,"syntology":{"n":14,"n_ran":14,"n_constructed":1,"n_ran_checked":2,"n_instrument":12,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"14 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; 12 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-many-faces-of-robustness-a-critical#ran","syntology_url":"https://syntology.ai/paper/2006.16241","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.16241"}},"official":{"repos":["hendrycks/imagenet-r"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/a-general-machine-learning-framework-for","slug":"a-general-machine-learning-framework-for","title":"A General Machine Learning Framework for Survival Analysis","date":"2020-06-27","arxiv_id":"2006.15442","repositories_listed":1,"syntology":null},{"url":"/paper/a-comparative-analysis-on-bangla-handwritten","slug":"a-comparative-analysis-on-bangla-handwritten","title":"A Comparative Analysis on Bangla Handwritten Digit Recognition with Data Augmentation and Non-Augmentation Process","date":"2020-06-26","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/explanation-augmented-feedback-in-human-in","slug":"explanation-augmented-feedback-in-human-in","title":"Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation","date":"2020-06-26","arxiv_id":"2006.14804","repositories_listed":1,"syntology":null},{"url":"/paper/lesion-mask-based-simultaneous-synthesis-of","slug":"lesion-mask-based-simultaneous-synthesis-of","title":"Lesion Mask-based Simultaneous Synthesis of Anatomic and MolecularMR Images using a GAN","date":"2020-06-26","arxiv_id":"2006.14761","repositories_listed":1,"syntology":null},{"url":"/paper/fast-accurate-and-simple-models-for-tabular","slug":"fast-accurate-and-simple-models-for-tabular","title":"Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation","date":"2020-06-25","arxiv_id":"2006.14284","repositories_listed":1,"syntology":null},{"url":"/paper/recurrent-quantum-neural-networks","slug":"recurrent-quantum-neural-networks","title":"Recurrent Quantum Neural Networks","date":"2020-06-25","arxiv_id":"2006.14619","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-data-augmentation-for","slug":"automatic-data-augmentation-for","title":"Automatic Data Augmentation for Generalization in Deep Reinforcement Learning","date":"2020-06-23","arxiv_id":"2006.12862","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/automatic-data-augmentation-for#ran","syntology_url":"https://syntology.ai/paper/2006.12862","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.12862"}},"official":{"repos":["rraileanu/auto-drac"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/contrastive-generative-adversarial-networks","slug":"contrastive-generative-adversarial-networks","title":"ContraGAN: Contrastive Learning for Conditional Image Generation","date":"2020-06-23","arxiv_id":"2006.12681","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"2 ran (of which 2 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; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/contrastive-generative-adversarial-networks#ran","syntology_url":"https://syntology.ai/paper/2006.12681","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.12681"}},"official":{"repos":["POSTECH-CVLab/PyTorch-StudioGAN"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/data-augmentation-view-on-graph-convolutional","slug":"data-augmentation-view-on-graph-convolutional","title":"Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph Learning","date":"2020-06-23","arxiv_id":"2006.13090","repositories_listed":1,"syntology":null},{"url":"/paper/discriminative-feature-alignment","slug":"discriminative-feature-alignment","title":"Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment","date":"2020-06-23","arxiv_id":"2006.12770","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/discriminative-feature-alignment#ran","syntology_url":"https://syntology.ai/paper/2006.12770","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.12770"}},"official":{"repos":["JingWang18/Discriminative-Feature-Alignment"],"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/realistic-adversarial-data-augmentation-for","slug":"realistic-adversarial-data-augmentation-for","title":"Realistic Adversarial Data Augmentation for MR Image Segmentation","date":"2020-06-23","arxiv_id":"2006.13322","repositories_listed":1,"syntology":null},{"url":"/paper/probabilistic-structural-latent-1","slug":"probabilistic-structural-latent-1","title":"Probabilistic Structural Latent Representation for Unsupervised Embedding","date":"2020-06-22","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/neural-topic-modeling-with-continual-lifelong","slug":"neural-topic-modeling-with-continual-lifelong","title":"Neural Topic Modeling with Continual Lifelong Learning","date":"2020-06-19","arxiv_id":"2006.10909","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},{"url":"/paper/lsd-c-linearly-separable-deep-clusters","slug":"lsd-c-linearly-separable-deep-clusters","title":"LSD-C: Linearly Separable Deep Clusters","date":"2020-06-17","arxiv_id":"2006.10039","repositories_listed":1,"syntology":null},{"url":"/paper/deepcapture-image-spam-detection-using-deep","slug":"deepcapture-image-spam-detection-using-deep","title":"DeepCapture: Image Spam Detection Using Deep Learning and Data Augmentation","date":"2020-06-16","arxiv_id":"2006.08885","repositories_listed":1,"syntology":null},{"url":"/paper/visual-chirality-1","slug":"visual-chirality-1","title":"Visual Chirality","date":"2020-06-16","arxiv_id":"2006.09512","repositories_listed":1,"syntology":null},{"url":"/paper/cascaded-deep-monocular-3d-human-pose-1","slug":"cascaded-deep-monocular-3d-human-pose-1","title":"Cascaded deep monocular 3D human pose estimation with evolutionary training data","date":"2020-06-14","arxiv_id":"2006.07778","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":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cascaded-deep-monocular-3d-human-pose-1#ran","syntology_url":"https://syntology.ai/paper/2006.07778","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.07778"}},"official":{"repos":["Nicholasli1995/EvoSkeleton"],"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/domain-generalization-using-causal-matching-1","slug":"domain-generalization-using-causal-matching-1","title":"Domain Generalization using Causal Matching","date":"2020-06-12","arxiv_id":"2006.07500","repositories_listed":1,"syntology":{"n":9,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":5,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/domain-generalization-using-causal-matching-1#ran","syntology_url":"https://syntology.ai/paper/2006.07500","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.07500"}},"official":{"repos":["microsoft/robustdg"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/cosda-ml-multi-lingual-code-switching-data","slug":"cosda-ml-multi-lingual-code-switching-data","title":"CoSDA-ML: Multi-Lingual Code-Switching Data Augmentation for Zero-Shot Cross-Lingual NLP","date":"2020-06-11","arxiv_id":"2006.06402","repositories_listed":1,"syntology":null},{"url":"/paper/on-mixup-regularization","slug":"on-mixup-regularization","title":"On Mixup Regularization","date":"2020-06-10","arxiv_id":"2006.06049","repositories_listed":1,"syntology":null},{"url":"/paper/towards-good-practices-for-data-augmentation","slug":"towards-good-practices-for-data-augmentation","title":"On Data Augmentation for GAN Training","date":"2020-06-09","arxiv_id":"2006.05338","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":3,"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) · 3 unverified","sample_list":"/paper/towards-good-practices-for-data-augmentation#ran","syntology_url":"https://syntology.ai/paper/2006.05338","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.05338"}},"official":{"repos":["sutd-visual-computing-group/dag-gans"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/a-transductive-multi-head-model-for-cross","slug":"a-transductive-multi-head-model-for-cross","title":"A Transductive Multi-Head Model for Cross-Domain Few-Shot Learning","date":"2020-06-08","arxiv_id":"2006.11384","repositories_listed":1,"syntology":null},{"url":"/paper/learning-diagnosis-of-covid-19-from-a-single","slug":"learning-diagnosis-of-covid-19-from-a-single","title":"Learning Diagnosis of COVID-19 from a Single Radiological Image","date":"2020-06-06","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-training-of-graph","slug":"self-supervised-training-of-graph","title":"Self-supervised Training of Graph Convolutional Networks","date":"2020-06-03","arxiv_id":"2006.02380","repositories_listed":1,"syntology":null},{"url":"/paper/augmenting-dl-with-adversarial-training-for","slug":"augmenting-dl-with-adversarial-training-for","title":"Augmenting DL with Adversarial Training for Robust Prediction of Epilepsy Seizures","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/automatic-classification-between-covid-19","slug":"automatic-classification-between-covid-19","title":"Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods","date":"2020-06-01","arxiv_id":"2006.00730","repositories_listed":1,"syntology":null},{"url":"/paper/bayesian-adversarial-human-motion-synthesis","slug":"bayesian-adversarial-human-motion-synthesis","title":"Bayesian Adversarial Human Motion Synthesis","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/composing-good-shots-by-exploiting-mutual","slug":"composing-good-shots-by-exploiting-mutual","title":"Composing Good Shots by Exploiting Mutual Relations","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/smoothmix-a-simple-yet-effective-data","slug":"smoothmix-a-simple-yet-effective-data","title":"SmoothMix: A Simple Yet Effective Data Augmentation to Train Robust Classifiers","date":"2020-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-joint-pixel-and-feature-alignment-framework","slug":"a-joint-pixel-and-feature-alignment-framework","title":"A Joint Pixel and Feature Alignment Framework for Cross-dataset Palmprint Recognition","date":"2020-05-25","arxiv_id":"2005.12044","repositories_listed":1,"syntology":null},{"url":"/paper/networks-with-pixels-embedding-a-method-to","slug":"networks-with-pixels-embedding-a-method-to","title":"Networks with pixels embedding: a method to improve noise resistance in images classification","date":"2020-05-24","arxiv_id":"2005.11679","repositories_listed":1,"syntology":null},{"url":"/paper/deltapy-a-framework-for-tabular-data","slug":"deltapy-a-framework-for-tabular-data","title":"DeltaPy: A Framework for Tabular Data Augmentation in Python","date":"2020-05-22","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/fluent-response-generation-for-conversational","slug":"fluent-response-generation-for-conversational","title":"Fluent Response Generation for Conversational Question Answering","date":"2020-05-21","arxiv_id":"2005.10464","repositories_listed":1,"syntology":null},{"url":"/paper/automl-segmentation-for-3d-medical-image-data","slug":"automl-segmentation-for-3d-medical-image-data","title":"AutoML Segmentation for 3D Medical Image Data: Contribution to the MSD Challenge 2018","date":"2020-05-20","arxiv_id":"2005.09978","repositories_listed":1,"syntology":null},{"url":"/paper/what-makes-for-good-views-for-contrastive","slug":"what-makes-for-good-views-for-contrastive","title":"What Makes for Good Views for Contrastive Learning?","date":"2020-05-20","arxiv_id":"2005.10243","repositories_listed":1,"syntology":null},{"url":"/paper/iterative-pseudo-labeling-for-speech","slug":"iterative-pseudo-labeling-for-speech","title":"Iterative Pseudo-Labeling for Speech Recognition","date":"2020-05-19","arxiv_id":"2005.09267","repositories_listed":1,"syntology":null},{"url":"/paper/fucitnet-improving-the-generalization-of-deep","slug":"fucitnet-improving-the-generalization-of-deep","title":"FuCiTNet: Improving the generalization of deep learning networks by the fusion of learned class-inherent transformations","date":"2020-05-17","arxiv_id":"2005.08235","repositories_listed":1,"syntology":null},{"url":"/paper/global-inducing-point-variational-posteriors","slug":"global-inducing-point-variational-posteriors","title":"Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes","date":"2020-05-17","arxiv_id":"2005.08140","repositories_listed":1,"syntology":{"n":13,"n_ran":6,"n_constructed":4,"n_ran_checked":4,"n_instrument":2,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":13,"phrase":"6 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/global-inducing-point-variational-posteriors#ran","syntology_url":"https://syntology.ai/paper/2005.08140","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.08140"}},"official":{"repos":["LaurenceA/bayesfunc"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/blind-source-extraction-based-on-multi","slug":"blind-source-extraction-based-on-multi","title":"Target Speech Extraction Based on Blind Source Separation and X-vector-based Speaker Selection Trained with Data Augmentation","date":"2020-05-16","arxiv_id":"2005.07976","repositories_listed":1,"syntology":null},{"url":"/paper/nat-noise-aware-training-for-robust-neural","slug":"nat-noise-aware-training-for-robust-neural","title":"NAT: Noise-Aware Training for Robust Neural Sequence Labeling","date":"2020-05-14","arxiv_id":"2005.07162","repositories_listed":1,"syntology":null},{"url":"/paper/parallel-data-augmentation-for-formality","slug":"parallel-data-augmentation-for-formality","title":"Parallel Data Augmentation for Formality Style Transfer","date":"2020-05-14","arxiv_id":"2005.07522","repositories_listed":1,"syntology":null},{"url":"/paper/viraal-virtual-adversarial-active-learning","slug":"viraal-virtual-adversarial-active-learning","title":"VirAAL: Virtual Adversarial Active Learning For NLU","date":"2020-05-14","arxiv_id":"2005.07287","repositories_listed":1,"syntology":null},{"url":"/paper/one-shot-recognition-of-manufacturing-defects","slug":"one-shot-recognition-of-manufacturing-defects","title":"One-Shot Recognition of Manufacturing Defects in Steel Surfaces","date":"2020-05-12","arxiv_id":"2005.05815","repositories_listed":1,"syntology":null},{"url":"/paper/ecg-delnet-delineation-of-ambulatory","slug":"ecg-delnet-delineation-of-ambulatory","title":"ECG-DelNet: Delineation of Ambulatory Electrocardiograms with Mixed Quality Labeling Using Neural Networks","date":"2020-05-11","arxiv_id":"2005.05236","repositories_listed":1,"syntology":null},{"url":"/paper/towards-robustifying-nli-models-against","slug":"towards-robustifying-nli-models-against","title":"Towards Robustifying NLI Models Against Lexical Dataset Biases","date":"2020-05-10","arxiv_id":"2005.04732","repositories_listed":1,"syntology":{"n":11,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":7,"n_honours":0,"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; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/towards-robustifying-nli-models-against#ran","syntology_url":"https://syntology.ai/paper/2005.04732","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.04732"}},"official":{"repos":["owenzx/LexicalDebias-ACL2020"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":7,"ran_from_kinds":["official"]}}}],"record_sha256":"8ab22e525f19462e28428c6c8d5d5ce4dae95265327f2875ca8c0b4948e9112b","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}