{"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/31","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":31,"pages_in_order":84,"rows_per_page":100,"rows":[3001,3100],"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/30","next":"/task/data-augmentation/papers/32","papers":[{"url":"/paper/gankyoku-a-generative-adversarial-network-for","slug":"gankyoku-a-generative-adversarial-network-for","title":"GANkyoku: a Generative Adversarial Network for Shakuhachi Music","date":"2019-11-22","arxiv_id":"1911.10119","repositories_listed":1,"syntology":null},{"url":"/paper/action-recognition-using-volumetric-motion","slug":"action-recognition-using-volumetric-motion","title":"Action Recognition Using Volumetric Motion Representations","date":"2019-11-19","arxiv_id":"1911.08511","repositories_listed":1,"syntology":null},{"url":"/paper/constructing-multiple-tasks-for-augmentation","slug":"constructing-multiple-tasks-for-augmentation","title":"Constructing Multiple Tasks for Augmentation: Improving Neural Image Classification With K-means Features","date":"2019-11-18","arxiv_id":"1911.07518","repositories_listed":1,"syntology":null},{"url":"/paper/faster-autoaugment-learning-augmentation","slug":"faster-autoaugment-learning-augmentation","title":"Faster AutoAugment: Learning Augmentation Strategies using Backpropagation","date":"2019-11-16","arxiv_id":"1911.06987","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 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; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/faster-autoaugment-learning-augmentation#ran","syntology_url":"https://syntology.ai/paper/1911.06987","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.06987"}},"official":null}},{"url":"/paper/logo-2k-a-large-scale-logo-dataset-for","slug":"logo-2k-a-large-scale-logo-dataset-for","title":"Logo-2K+: A Large-Scale Logo Dataset for Scalable Logo Classification","date":"2019-11-11","arxiv_id":"1911.07924","repositories_listed":1,"syntology":null},{"url":"/paper/towards-understanding-gender-bias-in-relation","slug":"towards-understanding-gender-bias-in-relation","title":"Towards Understanding Gender Bias in Relation Extraction","date":"2019-11-09","arxiv_id":"1911.03642","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"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) · 3 unverified","sample_list":"/paper/towards-understanding-gender-bias-in-relation#ran","syntology_url":"https://syntology.ai/paper/1911.03642","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.03642"}},"official":null}},{"url":"/paper/xceptiontime-a-novel-deep-architecture-based","slug":"xceptiontime-a-novel-deep-architecture-based","title":"XceptionTime: A Novel Deep Architecture based on Depthwise Separable Convolutions for Hand Gesture Classification","date":"2019-11-09","arxiv_id":"1911.03803","repositories_listed":1,"syntology":null},{"url":"/paper/not-enough-data-deep-learning-to-the-rescue","slug":"not-enough-data-deep-learning-to-the-rescue","title":"Not Enough Data? Deep Learning to the Rescue!","date":"2019-11-08","arxiv_id":"1911.03118","repositories_listed":1,"syntology":null},{"url":"/paper/roimix-proposal-fusion-among-multiple-images","slug":"roimix-proposal-fusion-among-multiple-images","title":"RoIMix: Proposal-Fusion among Multiple Images for Underwater Object Detection","date":"2019-11-08","arxiv_id":"1911.03029","repositories_listed":1,"syntology":null},{"url":"/paper/sentilr-linguistic-knowledge-enhanced","slug":"sentilr-linguistic-knowledge-enhanced","title":"SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge","date":"2019-11-06","arxiv_id":"1911.02493","repositories_listed":1,"syntology":null},{"url":"/paper/coreference-resolution-as-query-based-span","slug":"coreference-resolution-as-query-based-span","title":"Coreference Resolution as Query-based Span Prediction","date":"2019-11-05","arxiv_id":"1911.01746","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-annotate-modularizing-data","slug":"learning-to-annotate-modularizing-data","title":"Learning from Explanations with Neural Execution Tree","date":"2019-11-04","arxiv_id":"1911.01352","repositories_listed":1,"syntology":null},{"url":"/paper/improving-neural-machine-translation-1","slug":"improving-neural-machine-translation-1","title":"Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back-Translation","date":"2019-11-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/cross-domain-face-synthesis-using-a","slug":"cross-domain-face-synthesis-using-a","title":"Cross-Domain Face Synthesis using a Controllable GAN","date":"2019-10-31","arxiv_id":"1910.14247","repositories_listed":1,"syntology":null},{"url":"/paper/adapting-multilingual-neural-machine","slug":"adapting-multilingual-neural-machine","title":"Adapting Multilingual Neural Machine Translation to Unseen Languages","date":"2019-10-30","arxiv_id":"1910.13998","repositories_listed":1,"syntology":null},{"url":"/paper/discriminative-neural-clustering-for-speaker","slug":"discriminative-neural-clustering-for-speaker","title":"Discriminative Neural Clustering for Speaker Diarisation","date":"2019-10-22","arxiv_id":"1910.09703","repositories_listed":1,"syntology":null},{"url":"/paper/improving-singing-voice-separation-with-the","slug":"improving-singing-voice-separation-with-the","title":"Improving singing voice separation with the Wave-U-Net using Minimum Hyperspherical Energy","date":"2019-10-22","arxiv_id":"1910.10071","repositories_listed":1,"syntology":null},{"url":"/paper/miscnn-a-framework-for-medical-image","slug":"miscnn-a-framework-for-medical-image","title":"MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning","date":"2019-10-21","arxiv_id":"1910.09308","repositories_listed":1,"syntology":null},{"url":"/paper/monalog-a-lightweight-system-for-natural","slug":"monalog-a-lightweight-system-for-natural","title":"MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity","date":"2019-10-19","arxiv_id":"1910.08772","repositories_listed":1,"syntology":null},{"url":"/paper/real-time-lip-sync-for-live-2d-animation","slug":"real-time-lip-sync-for-live-2d-animation","title":"Real-Time Lip Sync for Live 2D Animation","date":"2019-10-19","arxiv_id":"1910.08685","repositories_listed":1,"syntology":null},{"url":"/paper/towards-more-sample-efficiency","slug":"towards-more-sample-efficiency","title":"Towards More Sample Efficiency in Reinforcement Learning with Data Augmentation","date":"2019-10-19","arxiv_id":"1910.09959","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-data-augmentation-by-learning-the","slug":"automatic-data-augmentation-by-learning-the","title":"Automatic Data Augmentation by Learning the Deterministic Policy","date":"2019-10-18","arxiv_id":"1910.08343","repositories_listed":1,"syntology":null},{"url":"/paper/illumination-based-data-augmentation-for","slug":"illumination-based-data-augmentation-for","title":"Illumination-Based Data Augmentation for Robust Background Subtraction","date":"2019-10-18","arxiv_id":"1910.08470","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-data-augmentation-self-supervision-1","slug":"rethinking-data-augmentation-self-supervision-1","title":"Self-supervised Label Augmentation via Input Transformations","date":"2019-10-14","arxiv_id":"1910.05872","repositories_listed":1,"syntology":null},{"url":"/paper/generative-image-translation-for-data-1","slug":"generative-image-translation-for-data-1","title":"Generative Image Translation for Data Augmentation in Colorectal Histopathology Images","date":"2019-10-13","arxiv_id":"1910.05827","repositories_listed":1,"syntology":null},{"url":"/paper/cross-domain-image-classification-through","slug":"cross-domain-image-classification-through","title":"Cross-Domain Image Classification through Neural-Style Transfer Data Augmentation","date":"2019-10-12","arxiv_id":"1910.05611","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-pulmonary-pathology-translation","slug":"adversarial-pulmonary-pathology-translation","title":"Adversarial Pulmonary Pathology Translation for Pairwise Chest X-ray Data Augmentation","date":"2019-10-11","arxiv_id":"1910.04961","repositories_listed":1,"syntology":null},{"url":"/paper/anda-a-novel-data-augmentation-technique","slug":"anda-a-novel-data-augmentation-technique","title":"ANDA: A Novel Data Augmentation Technique Applied to Salient Object Detection","date":"2019-10-03","arxiv_id":"1910.01256","repositories_listed":1,"syntology":null},{"url":"/paper/over-parameterization-as-a-catalyst-for","slug":"over-parameterization-as-a-catalyst-for","title":"Student Specialization in Deep ReLU Networks With Finite Width and Input Dimension","date":"2019-09-30","arxiv_id":"1909.13458","repositories_listed":1,"syntology":null},{"url":"/paper/automatically-learning-data-augmentation","slug":"automatically-learning-data-augmentation","title":"Automatically Learning Data Augmentation Policies for Dialogue Tasks","date":"2019-09-27","arxiv_id":"1909.12868","repositories_listed":1,"syntology":null},{"url":"/paper/urban-sound-tagging-using-convolutional","slug":"urban-sound-tagging-using-convolutional","title":"Urban Sound Tagging using Convolutional Neural Networks","date":"2019-09-27","arxiv_id":"1909.12699","repositories_listed":1,"syntology":null},{"url":"/paper/implicit-semantic-data-augmentation-for-deep","slug":"implicit-semantic-data-augmentation-for-deep","title":"Implicit Semantic Data Augmentation for Deep Networks","date":"2019-09-26","arxiv_id":"1909.12220","repositories_listed":1,"syntology":null},{"url":"/paper/accept-synthetic-objects-as-real-end-to-end","slug":"accept-synthetic-objects-as-real-end-to-end","title":"Accept Synthetic Objects as Real: End-to-End Training of Attentive Deep Visuomotor Policies for Manipulation in Clutter","date":"2019-09-24","arxiv_id":"1909.11128","repositories_listed":1,"syntology":null},{"url":"/paper/direct-training-based-spiking-convolutional","slug":"direct-training-based-spiking-convolutional","title":"Temporal-Coded Deep Spiking Neural Network with Easy Training and Robust Performance","date":"2019-09-24","arxiv_id":"1909.10837","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 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; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/direct-training-based-spiking-convolutional#ran","syntology_url":"https://syntology.ai/paper/1909.10837","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.10837"}},"official":{"repos":["zbs881314/Temporal-Coded-Deep-SNN"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/invariant-transform-experience-replay","slug":"invariant-transform-experience-replay","title":"Invariant Transform Experience Replay: Data Augmentation for Deep Reinforcement Learning","date":"2019-09-24","arxiv_id":"1909.10707","repositories_listed":1,"syntology":null},{"url":"/paper/190909725","slug":"190909725","title":"Context-Aware Image Matting for Simultaneous Foreground and Alpha Estimation","date":"2019-09-20","arxiv_id":"1909.09725","repositories_listed":1,"syntology":null},{"url":"/paper/an-unpaired-sketch-to-photo-translation-model","slug":"an-unpaired-sketch-to-photo-translation-model","title":"Unsupervised Sketch-to-Photo Synthesis","date":"2019-09-18","arxiv_id":"1909.08313","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":6,"n_pointer_only":3,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 1 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/an-unpaired-sketch-to-photo-translation-model#ran","syntology_url":"https://syntology.ai/paper/1909.08313","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.08313"}},"official":null}},{"url":"/paper/espresso-a-fast-end-to-end-neural-speech","slug":"espresso-a-fast-end-to-end-neural-speech","title":"Espresso: A Fast End-to-end Neural Speech Recognition Toolkit","date":"2019-09-18","arxiv_id":"1909.08723","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/espresso-a-fast-end-to-end-neural-speech#ran","syntology_url":"https://syntology.ai/paper/1909.08723","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.08723"}},"official":{"repos":["freewym/espresso"],"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":["unlocated"]}}},{"url":"/paper/progressive-fusion-for-unsupervised-binocular","slug":"progressive-fusion-for-unsupervised-binocular","title":"Progressive Fusion for Unsupervised Binocular Depth Estimation using Cycled Networks","date":"2019-09-17","arxiv_id":"1909.07667","repositories_listed":1,"syntology":null},{"url":"/paper/bridging-the-domain-gap-in-cross-lingual","slug":"bridging-the-domain-gap-in-cross-lingual","title":"Bridging the domain gap in cross-lingual document classification","date":"2019-09-16","arxiv_id":"1909.07009","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/bridging-the-domain-gap-in-cross-lingual#ran","syntology_url":"https://syntology.ai/paper/1909.07009","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.07009"}},"official":{"repos":["laiguokun/xlu-data"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/reinforcement-learning-for-portfolio","slug":"reinforcement-learning-for-portfolio","title":"Reinforcement Learning for Portfolio Management","date":"2019-09-12","arxiv_id":"1909.09571","repositories_listed":1,"syntology":null},{"url":"/paper/an-active-learning-approach-for-reducing","slug":"an-active-learning-approach-for-reducing","title":"An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysis","date":"2019-09-05","arxiv_id":"1909.02344","repositories_listed":1,"syntology":null},{"url":"/paper/an-efficient-and-layout-independent-automatic","slug":"an-efficient-and-layout-independent-automatic","title":"An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector","date":"2019-09-04","arxiv_id":"1909.01754","repositories_listed":1,"syntology":null},{"url":"/paper/a-geometry-sensitive-approach-for","slug":"a-geometry-sensitive-approach-for","title":"A Geometry-Sensitive Approach for Photographic Style Classification","date":"2019-09-03","arxiv_id":"1909.01040","repositories_listed":1,"syntology":null},{"url":"/paper/achieving-verified-robustness-to-symbol","slug":"achieving-verified-robustness-to-symbol","title":"Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation","date":"2019-09-03","arxiv_id":"1909.01492","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/achieving-verified-robustness-to-symbol#ran","syntology_url":"https://syntology.ai/paper/1909.01492","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.01492"}},"official":{"repos":["deepmind/interval-bound-propagation"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/handling-syntactic-divergence-in-low-resource","slug":"handling-syntactic-divergence-in-low-resource","title":"Handling Syntactic Divergence in Low-resource Machine Translation","date":"2019-08-30","arxiv_id":"1909.00040","repositories_listed":1,"syntology":null},{"url":"/paper/keep-calm-and-switch-on-preserving-sentiment","slug":"keep-calm-and-switch-on-preserving-sentiment","title":"Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text Exchange","date":"2019-08-30","arxiv_id":"1909.00088","repositories_listed":1,"syntology":null},{"url":"/paper/data-augmentation-with-atomic-templates-for","slug":"data-augmentation-with-atomic-templates-for","title":"Data Augmentation with Atomic Templates for Spoken Language Understanding","date":"2019-08-28","arxiv_id":"1908.10770","repositories_listed":1,"syntology":null},{"url":"/paper/depth-wise-separable-convolutions-and-multi","slug":"depth-wise-separable-convolutions-and-multi","title":"Depth-wise separable convolutions and multi-level pooling for an efficient spatial CNN-based steganalysis","date":"2019-08-22","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/a-kings-ransom-for-encryption-ransomware","slug":"a-kings-ransom-for-encryption-ransomware","title":"A Kings Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation","date":"2019-08-19","arxiv_id":"1908.06750","repositories_listed":1,"syntology":null},{"url":"/paper/multi-step-cascaded-networks-for-brain-tumor","slug":"multi-step-cascaded-networks-for-brain-tumor","title":"Multi-step Cascaded Networks for Brain Tumor Segmentation","date":"2019-08-16","arxiv_id":"1908.05887","repositories_listed":1,"syntology":null},{"url":"/paper/distinction-maximization-loss-fast-scalable","slug":"distinction-maximization-loss-fast-scalable","title":"Isotropy Maximization Loss and Entropic Score: Accurate, Fast, Efficient, Scalable, and Turnkey Neural Networks Out-of-Distribution Detection Based on The Principle of Maximum Entropy","date":"2019-08-15","arxiv_id":"1908.05569","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-evaluation-of-machine-translation","slug":"on-the-evaluation-of-machine-translation","title":"On The Evaluation of Machine Translation Systems Trained With Back-Translation","date":"2019-08-14","arxiv_id":"1908.05204","repositories_listed":1,"syntology":null},{"url":"/paper/combining-learned-skills-and-reinforcement","slug":"combining-learned-skills-and-reinforcement","title":"Learning to combine primitive skills: A step towards versatile robotic manipulation","date":"2019-08-02","arxiv_id":"1908.00722","repositories_listed":1,"syntology":null},{"url":"/paper/a-survey-on-deep-learning-of-small-sample-in","slug":"a-survey-on-deep-learning-of-small-sample-in","title":"A Survey on Deep Learning of Small Sample in Biomedical Image Analysis","date":"2019-08-01","arxiv_id":"1908.00473","repositories_listed":1,"syntology":null},{"url":"/paper/fill-the-gap-exploiting-bert-for-pronoun","slug":"fill-the-gap-exploiting-bert-for-pronoun","title":"Fill the GAP: Exploiting BERT for Pronoun Resolution","date":"2019-08-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/safe-augmentation-learning-task-specific","slug":"safe-augmentation-learning-task-specific","title":"Safe Augmentation: Learning Task-Specific Transformations from Data","date":"2019-07-30","arxiv_id":"1907.12896","repositories_listed":1,"syntology":null},{"url":"/paper/a-fully-convolutional-neural-network-for-2","slug":"a-fully-convolutional-neural-network-for-2","title":"BSUV-Net: A Fully-Convolutional Neural Network for Background Subtraction of Unseen Videos","date":"2019-07-26","arxiv_id":"1907.11371","repositories_listed":1,"syntology":null},{"url":"/paper/invariance-reduces-variance-understanding","slug":"invariance-reduces-variance-understanding","title":"A Group-Theoretic Framework for Data Augmentation","date":"2019-07-25","arxiv_id":"1907.10905","repositories_listed":1,"syntology":null},{"url":"/paper/a-cnn-based-tool-for-automatic-tongue-contour","slug":"a-cnn-based-tool-for-automatic-tongue-contour","title":"A CNN-based tool for automatic tongue contour tracking in ultrasound images","date":"2019-07-24","arxiv_id":"1907.10210","repositories_listed":1,"syntology":null},{"url":"/paper/effortless-deep-training-for-traffic-sign","slug":"effortless-deep-training-for-traffic-sign","title":"Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images","date":"2019-07-23","arxiv_id":"1907.09679","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-learning-by-disentangling-and","slug":"semi-supervised-learning-by-disentangling-and","title":"Semi-Supervised Learning by Disentangling and Self-Ensembling Over Stochastic Latent Space","date":"2019-07-22","arxiv_id":"1907.09607","repositories_listed":1,"syntology":null},{"url":"/paper/post-synaptic-potential-regularization-has","slug":"post-synaptic-potential-regularization-has","title":"Post-synaptic potential regularization has potential","date":"2019-07-19","arxiv_id":"1907.08544","repositories_listed":1,"syntology":null},{"url":"/paper/human-pose-estimation-for-real-world-crowded","slug":"human-pose-estimation-for-real-world-crowded","title":"Human Pose Estimation for Real-World Crowded Scenarios","date":"2019-07-16","arxiv_id":"1907.06922","repositories_listed":1,"syntology":null},{"url":"/paper/neural-language-model-based-training-data","slug":"neural-language-model-based-training-data","title":"Neural Language Model Based Training Data Augmentation for Weakly Supervised Early Rumor Detection","date":"2019-07-16","arxiv_id":"1907.07033","repositories_listed":1,"syntology":null},{"url":"/paper/deep-sequential-mosaicking-of-fetoscopic","slug":"deep-sequential-mosaicking-of-fetoscopic","title":"Deep Sequential Mosaicking of Fetoscopic Videos","date":"2019-07-15","arxiv_id":"1907.06543","repositories_listed":1,"syntology":null},{"url":"/paper/astraea-self-balancing-federated-learning-for","slug":"astraea-self-balancing-federated-learning-for","title":"Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applications","date":"2019-07-02","arxiv_id":"1907.01132","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/astraea-self-balancing-federated-learning-for#ran","syntology_url":"https://syntology.ai/paper/1907.01132","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.01132"}},"official":null}},{"url":"/paper/conan-counter-narratives-through","slug":"conan-counter-narratives-through","title":"CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech","date":"2019-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/improving-the-robustness-of-question","slug":"improving-the-robustness-of-question","title":"Improving the Robustness of Question Answering Systems to Question Paraphrasing","date":"2019-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/on-the-summarization-of-consumer-health","slug":"on-the-summarization-of-consumer-health","title":"On the Summarization of Consumer Health Questions","date":"2019-07-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/effective-rotation-invariant-point-cnn-with","slug":"effective-rotation-invariant-point-cnn-with","title":"Effective Rotation-invariant Point CNN with Spherical Harmonics kernels","date":"2019-06-27","arxiv_id":"1906.11555","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/effective-rotation-invariant-point-cnn-with#ran","syntology_url":"https://syntology.ai/paper/1906.11555","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.11555"}},"official":{"repos":["adrienPoulenard/SPHnet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/predicting-confusion-from-eye-tracking-data","slug":"predicting-confusion-from-eye-tracking-data","title":"Predicting Confusion from Eye-Tracking Data with Recurrent Neural Networks","date":"2019-06-19","arxiv_id":"1906.11211","repositories_listed":1,"syntology":null},{"url":"/paper/can-neural-networks-understand-monotonicity","slug":"can-neural-networks-understand-monotonicity","title":"Can neural networks understand monotonicity reasoning?","date":"2019-06-15","arxiv_id":"1906.06448","repositories_listed":1,"syntology":null},{"url":"/paper/learning-robust-visual-representations-using","slug":"learning-robust-visual-representations-using","title":"Learning robust visual representations using data augmentation invariance","date":"2019-06-11","arxiv_id":"1906.04547","repositories_listed":1,"syntology":null},{"url":"/paper/when-unseen-domain-generalization-is","slug":"when-unseen-domain-generalization-is","title":"When Unseen Domain Generalization is Unnecessary? Rethinking Data Augmentation","date":"2019-06-07","arxiv_id":"1906.03347","repositories_listed":1,"syntology":null},{"url":"/paper/bad-global-minima-exist-and-sgd-can-reach","slug":"bad-global-minima-exist-and-sgd-can-reach","title":"Bad Global Minima Exist and SGD Can Reach Them","date":"2019-06-06","arxiv_id":"1906.02613","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/bad-global-minima-exist-and-sgd-can-reach#ran","syntology_url":"https://syntology.ai/paper/1906.02613","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.02613"}},"official":{"repos":["chao1224/BadGlobalMinima"],"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/practical-deep-learning-with-bayesian","slug":"practical-deep-learning-with-bayesian","title":"Practical Deep Learning with Bayesian Principles","date":"2019-06-06","arxiv_id":"1906.02506","repositories_listed":1,"syntology":null},{"url":"/paper/selective-style-transfer-for-text","slug":"selective-style-transfer-for-text","title":"Selective Style Transfer for Text","date":"2019-06-04","arxiv_id":"1906.01466","repositories_listed":1,"syntology":null},{"url":"/paper/190600804","slug":"190600804","title":"DualDis: Dual-Branch Disentangling with Adversarial Learning","date":"2019-06-03","arxiv_id":"1906.00804","repositories_listed":1,"syntology":null},{"url":"/paper/190600358","slug":"190600358","title":"Data Augmentation for Object Detection via Progressive and Selective Instance-Switching","date":"2019-06-02","arxiv_id":"1906.00358","repositories_listed":1,"syntology":null},{"url":"/paper/improved-lexically-constrained-decoding-for","slug":"improved-lexically-constrained-decoding-for","title":"Improved Lexically Constrained Decoding for Translation and Monolingual Rewriting","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/submodular-optimization-based-diverse","slug":"submodular-optimization-based-diverse","title":"Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/training-data-augmentation-for-context","slug":"training-data-augmentation-for-context","title":"Training Data Augmentation for Context-Sensitive Neural Lemmatizer Using Inflection Tables and Raw Text","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/visual-attention-consistency-under-image","slug":"visual-attention-consistency-under-image","title":"Visual Attention Consistency Under Image Transforms for Multi-Label Image Classification","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/straight-to-shapes-real-time-instance","slug":"straight-to-shapes-real-time-instance","title":"Straight to Shapes++: Real-time Instance Segmentation Made More Accurate","date":"2019-05-27","arxiv_id":"1905.11358","repositories_listed":1,"syntology":null},{"url":"/paper/soft-contextual-data-augmentation-for-neural","slug":"soft-contextual-data-augmentation-for-neural","title":"Soft Contextual Data Augmentation for Neural Machine Translation","date":"2019-05-25","arxiv_id":"1905.10523","repositories_listed":1,"syntology":null},{"url":"/paper/contextual-out-of-domain-utterance-handling","slug":"contextual-out-of-domain-utterance-handling","title":"Contextual Out-of-Domain Utterance Handling With Counterfeit Data Augmentation","date":"2019-05-24","arxiv_id":"1905.10247","repositories_listed":1,"syntology":null},{"url":"/paper/augmenting-correlation-structures-in-spatial","slug":"augmenting-correlation-structures-in-spatial","title":"Augmenting correlation structures in spatial data using deep generative models","date":"2019-05-23","arxiv_id":"1905.09796","repositories_listed":1,"syntology":null},{"url":"/paper/robust-sound-event-detection-in-bioacoustic","slug":"robust-sound-event-detection-in-bioacoustic","title":"Robust sound event detection in bioacoustic sensor networks","date":"2019-05-20","arxiv_id":"1905.08352","repositories_listed":1,"syntology":null},{"url":"/paper/lidar-sensor-modeling-and-data-augmentation","slug":"lidar-sensor-modeling-and-data-augmentation","title":"LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving","date":"2019-05-17","arxiv_id":"1905.07290","repositories_listed":1,"syntology":null},{"url":"/paper/online-hyper-parameter-learning-for-auto","slug":"online-hyper-parameter-learning-for-auto","title":"Online Hyper-parameter Learning for Auto-Augmentation Strategy","date":"2019-05-17","arxiv_id":"1905.07373","repositories_listed":1,"syntology":null},{"url":"/paper/leverage-eye-movement-data-for-saliency","slug":"leverage-eye-movement-data-for-saliency","title":"How is Gaze Influenced by Image Transformations? Dataset and Model","date":"2019-05-16","arxiv_id":"1905.06803","repositories_listed":1,"syntology":null},{"url":"/paper/paganda-an-adaptive-task-independent","slug":"paganda-an-adaptive-task-independent","title":"PAGANDA: An Adaptive Task-Independent Automatic Data Augmentation","date":"2019-05-16","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-optimal-data-augmentation-policies","slug":"learning-optimal-data-augmentation-policies","title":"Learning Optimal Data Augmentation Policies via Bayesian Optimization for Image Classification Tasks","date":"2019-05-06","arxiv_id":"1905.02610","repositories_listed":1,"syntology":null},{"url":"/paper/drone-path-following-in-gps-denied","slug":"drone-path-following-in-gps-denied","title":"Drone Path-Following in GPS-Denied Environments using Convolutional Networks","date":"2019-05-05","arxiv_id":"1905.01658","repositories_listed":1,"syntology":null},{"url":"/paper/learn-to-synthesize-and-synthesize-to-learn","slug":"learn-to-synthesize-and-synthesize-to-learn","title":"Learn to synthesize and synthesize to learn","date":"2019-05-01","arxiv_id":"1905.00286","repositories_listed":1,"syntology":null},{"url":"/paper/appearance-and-pose-conditioned-human-image","slug":"appearance-and-pose-conditioned-human-image","title":"Appearance and Pose-Conditioned Human Image Generation using Deformable GANs","date":"2019-04-30","arxiv_id":"1905.00007","repositories_listed":1,"syntology":null},{"url":"/paper/learning-raw-image-denoising-with-bayer","slug":"learning-raw-image-denoising-with-bayer","title":"Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation","date":"2019-04-29","arxiv_id":"1904.12945","repositories_listed":1,"syntology":null},{"url":"/paper/help-a-dataset-for-identifying-shortcomings","slug":"help-a-dataset-for-identifying-shortcomings","title":"HELP: A Dataset for Identifying Shortcomings of Neural Models in Monotonicity Reasoning","date":"2019-04-27","arxiv_id":"1904.12166","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/help-a-dataset-for-identifying-shortcomings#ran","syntology_url":"https://syntology.ai/paper/1904.12166","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.12166"}},"official":{"repos":["verypluming/HELP"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/190411005","slug":"190411005","title":"Analytical Moment Regularizer for Gaussian Robust Networks","date":"2019-04-24","arxiv_id":"1904.11005","repositories_listed":1,"syntology":null}],"record_sha256":"1e12be2e5e3e439c751c5d6524ed0c737452140dea7a37c5ab425e1db5847180","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}