{"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/66","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":66,"pages_in_order":84,"rows_per_page":100,"rows":[6501,6600],"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/65","next":"/task/data-augmentation/papers/67","papers":[{"url":null,"slug":"autocog-a-unified-data-modal-co-search","title":"AutoCoG: A Unified Data-Modal Co-Search Framework for Graph Neural Networks","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"autoregressive-latent-video-prediction-with","title":"Autoregressive Latent Video Prediction with High-Fidelity Image Generator","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"best-practices-in-pool-based-active-learning","title":"Best Practices in Pool-based Active Learning for Image Classification","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"causaldyna-improving-generalization-of-dyna","title":"CausalDyna: Improving Generalization of Dyna-style Reinforcement Learning via Counterfactual-Based Data Augmentation","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-learning-is-just-meta-learning","title":"Contrastive Learning is Just Meta-Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"crossmatch-cross-classifier-consistency","title":"CrossMatch: Cross-Classifier Consistency Regularization for Open-Set Single Domain Generalization","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cyclic-test-time-augmentation-with-entropy","title":"Cyclic Test Time Augmentation with Entropy Weight Method","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-convolutional-recurrent-neural-network","title":"Deep convolutional recurrent neural network for short-interval EEG motor imagery classification","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dm-ct-consistency-training-with-data-and","title":"DM-CT: Consistency Training with Data and Model Perturbation","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-out-of-distribution-detection-via","title":"Efficient Out-of-Distribution Detection via CVAE data Generation","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"identity-disentangled-adversarial","title":"Identity-Disentangled Adversarial Augmentation for Self-supervised Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-discriminative-visual","title":"Improving Discriminative Visual Representation Learning via Automatic Mixup","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-out-of-distribution-robustness-of","title":"Improving Out-of-Distribution Robustness of Classifiers Through Interpolated Generative Models","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"iterative-bilinear-temporal-spectral-fusion","title":"Iterative Bilinear Temporal-Spectral Fusion for Unsupervised Representation Learning in Time Series","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-feature-disentanglement-for-visual","title":"Latent Feature Disentanglement For Visual Domain Generalization","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learnability-and-expressiveness-in-self","title":"Learnability and Expressiveness in Self-Supervised Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mistake-driven-image-classification-with","title":"Mistake-driven Image Classification with FastGAN and SpinalNet","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"momentum-as-variance-reduced-stochastic","title":"Momentum as Variance-Reduced Stochastic Gradient","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-task-distribution-learning","title":"Multi-Task Distribution Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"neuro-symbolic-ontology-mediated-query","title":"Neuro-Symbolic Ontology-Mediated Query Answering","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"noisy-adversarial-training","title":"Noisy Adversarial Training","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"piecing-and-chipping-an-effective-solution","title":"Piecing and Chipping: An effective solution for the information-erasing view generation in Self-supervised Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sample-specific-and-context-aware","title":"Sample-specific and Context-aware Augmentation for Long Tail Image Classification","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"segmentation-of-roads-in-satellite-images","title":"Segmentation of Roads in Satellite Images using specially modified U-Net CNNs","date":"2021-09-29","arxiv_id":"2109.14671","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-learning-for-sequential","title":"Self-supervised Learning for Sequential Recommendation with Model Augmentation","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-learning-of-motion-informed","title":"Self-Supervised Learning of Motion-Informed Latents","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sketchode-learning-neural-sketch","title":"SketchODE: Learning neural sketch representation in continuous time","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"synclr-a-synthesis-framework-for-contrastive","title":"SynCLR: A Synthesis Framework for Contrastive Learning of out-of-domain Speech Representations","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"theoretical-analysis-of-consistency","title":"Theoretical Analysis of Consistency Regularization with Limited Augmented Data","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-robust-point-cloud-models-with","title":"Towards Robust Point Cloud Models with Context-Consistency Network and Adaptive Augmentation","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-overfitting-in-reweighting","title":"Understanding Overfitting in Reweighting Algorithms for Worst-group Performance","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-the-generalization-gap-in","title":"Understanding the Generalization Gap in Visual Reinforcement Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-the-success-of-knowledge","title":"Understanding the Success of Knowledge Distillation -- A Data Augmentation Perspective","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"vicinal-counting-networks","title":"Vicinal Counting Networks","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"what-makes-better-augmentation-strategies","title":"What Makes Better Augmentation Strategies? Augment Difficult but Not too Different","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"where-is-the-bottleneck-in-long-tailed","title":"Where is the bottleneck in long-tailed classification?","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"who-is-your-right-mixup-partner-in-positive","title":"Who Is Your Right Mixup Partner in Positive and Unlabeled Learning","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-background-invariance-improves","title":"Learning Background Invariance Improves Generalization and Robustness in Self-Supervised Learning on ImageNet and Beyond","date":"2021-09-28","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"prediction-of-the-facial-growth-direction-is","title":"Prediction of the Facial Growth Direction is Challenging","date":"2021-09-28","arxiv_id":"2110.02316","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-network-training-approach-for-open","title":"A novel network training approach for open set image recognition","date":"2021-09-27","arxiv_id":"2109.12756","repositories_listed":0,"syntology":null},{"url":null,"slug":"layer-parallel-training-of-residual-networks","title":"Layer-Parallel Training of Residual Networks with Auxiliary Variables","date":"2021-09-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"damix-density-aware-data-augmentation-for","title":"DAMix: A Density-Aware Mixup Augmentation for Single Image Dehazing under Domain Shift","date":"2021-09-26","arxiv_id":"2109.12544","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-diversity-enhanced-and-constraints-relaxed","title":"A Diversity-Enhanced and Constraints-Relaxed Augmentation for Low-Resource Classification","date":"2021-09-24","arxiv_id":"2109.11834","repositories_listed":0,"syntology":null},{"url":null,"slug":"dense-contrastive-visual-linguistic","title":"Dense Contrastive Visual-Linguistic Pretraining","date":"2021-09-24","arxiv_id":"2109.11778","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-cross-modality-domain-adaptation-1","title":"Unsupervised Cross-Modality Domain Adaptation for Segmenting Vestibular Schwannoma and Cochlea with Data Augmentation and Model Ensemble","date":"2021-09-24","arxiv_id":"2109.12169","repositories_listed":0,"syntology":null},{"url":null,"slug":"channelaugment-improving-generalization-of","title":"ChannelAugment: Improving generalization of multi-channel ASR by training with input channel randomization","date":"2021-09-23","arxiv_id":"2109.11225","repositories_listed":0,"syntology":null},{"url":null,"slug":"distiller-a-systematic-study-of-model","title":"Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing","date":"2021-09-23","arxiv_id":"2109.11105","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-view-contrastive-self-supervised","title":"Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks","date":"2021-09-23","arxiv_id":"2109.11201","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-robust-agents-for-visual-navigation","title":"Benchmarking Augmentation Methods for Learning Robust Navigation Agents: the Winning Entry of the 2021 iGibson Challenge","date":"2021-09-22","arxiv_id":"2109.10493","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-ultra-fast-method-for-simulation-of","title":"An Ultra-Fast Method for Simulation of Realistic Ultrasound Images","date":"2021-09-21","arxiv_id":"2109.10353","repositories_listed":0,"syntology":null},{"url":null,"slug":"demonstration-efficient-guided-policy-search","title":"Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC","date":"2021-09-21","arxiv_id":"2109.09910","repositories_listed":0,"syntology":null},{"url":null,"slug":"digital-signal-processing-using-deep-neural","title":"Digital Signal Processing Using Deep Neural Networks","date":"2021-09-21","arxiv_id":"2109.10404","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-person-pose-estimation-a-survey","title":"Single Person Pose Estimation: A Survey","date":"2021-09-21","arxiv_id":"2109.10056","repositories_listed":0,"syntology":null},{"url":null,"slug":"a2log-attentive-augmented-log-anomaly","title":"A2Log: Attentive Augmented Log Anomaly Detection","date":"2021-09-20","arxiv_id":"2109.09537","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-methods-for-anaphoric-zero","title":"Data Augmentation Methods for Anaphoric Zero Pronouns","date":"2021-09-20","arxiv_id":"2109.09825","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-gesture-recognition","title":"Dynamic Gesture Recognition","date":"2021-09-20","arxiv_id":"2109.09396","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-zero-label-language-learning","title":"Towards Zero-Label Language Learning","date":"2021-09-19","arxiv_id":"2109.09193","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-data-augmentation-and-deep-attention","title":"Hybrid Data Augmentation and Deep Attention-based Dilated Convolutional-Recurrent Neural Networks for Speech Emotion Recognition","date":"2021-09-18","arxiv_id":"2109.09026","repositories_listed":0,"syntology":null},{"url":null,"slug":"digging-errors-in-nmt-evaluating-and","title":"Digging Errors in NMT: Evaluating and Understanding Model Errors from Hypothesis Distribution","date":"2021-09-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"draft-command-and-edit-controllable-text","title":"Draft, Command, and Edit: Controllable Text Editing in E-Commerce","date":"2021-09-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"flipda-effective-and-robust-data-augmentation-1","title":"FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning","date":"2021-09-17","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"mitigating-data-scarceness-through-data","title":"Mitigating Data Scarceness through Data Synthesis, Augmentation and Curriculum for Abstractive Summarization","date":"2021-09-17","arxiv_id":"2109.08569","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-network-based-lidar-gesture","title":"Neural Network Based Lidar Gesture Recognition for Realtime Robot Teleoperation","date":"2021-09-17","arxiv_id":"2109.08263","repositories_listed":0,"syntology":null},{"url":null,"slug":"primary-tumor-and-inter-organ-augmentations","title":"Primary Tumor and Inter-Organ Augmentations for Supervised Lymph Node Colon Adenocarcinoma Metastasis Detection","date":"2021-09-17","arxiv_id":"2109.09518","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-few-shot-intent","title":"Semi-Supervised Few-Shot Intent Classification and Slot Filling","date":"2021-09-17","arxiv_id":"2109.08754","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-jhu-microsoft-submission-for-wmt21","title":"The JHU-Microsoft Submission for WMT21 Quality Estimation Shared Task","date":"2021-09-17","arxiv_id":"2109.08724","repositories_listed":0,"syntology":null},{"url":null,"slug":"pdaugment-data-augmentation-by-pitch-and","title":"PDAugment: Data Augmentation by Pitch and Duration Adjustments for Automatic Lyrics Transcription","date":"2021-09-16","arxiv_id":"2109.07940","repositories_listed":0,"syntology":null},{"url":null,"slug":"bert-is-robust-a-case-against-synonym-based","title":"BERT is Robust! A Case Against Synonym-Based Adversarial Examples in Text Classification","date":"2021-09-15","arxiv_id":"2109.07403","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-contrastive-learning-for","title":"Federated Contrastive Learning for Decentralized Unlabeled Medical Images","date":"2021-09-15","arxiv_id":"2109.07504","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-three-step-training-approach-with-data","title":"A Three Step Training Approach with Data Augmentation for Morphological Inflection","date":"2021-09-14","arxiv_id":"2109.07006","repositories_listed":0,"syntology":null},{"url":null,"slug":"conditional-synthetic-data-generation-for","title":"Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data","date":"2021-09-14","arxiv_id":"2109.06486","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-bone-length-attack-on-action","title":"Adversarial Bone Length Attack on Action Recognition","date":"2021-09-13","arxiv_id":"2109.05830","repositories_listed":0,"syntology":null},{"url":null,"slug":"contrastive-learning-for-context-aware-neural","title":"Contrastive Learning for Context-aware Neural Machine TranslationUsing Coreference Information","date":"2021-09-13","arxiv_id":"2109.05712","repositories_listed":0,"syntology":null},{"url":null,"slug":"dha-end-to-end-joint-optimization-of-data","title":"DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture","date":"2021-09-13","arxiv_id":"2109.05765","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-few-shot-learning-with","title":"Fine-Grained Few Shot Learning with Foreground Object Transformation","date":"2021-09-13","arxiv_id":"2109.05719","repositories_listed":0,"syntology":null},{"url":null,"slug":"differential-diagnosis-of-frontotemporal","title":"Differential Diagnosis of Frontotemporal Dementia and Alzheimer's Disease using Generative Adversarial Network","date":"2021-09-12","arxiv_id":"2109.05627","repositories_listed":0,"syntology":null},{"url":null,"slug":"good-enough-example-extrapolation","title":"Good-Enough Example Extrapolation","date":"2021-09-12","arxiv_id":"2109.05602","repositories_listed":0,"syntology":null},{"url":null,"slug":"stylistic-retrieval-based-dialogue-system","title":"Stylistic Retrieval-based Dialogue System with Unparallel Training Data","date":"2021-09-12","arxiv_id":"2109.05477","repositories_listed":0,"syntology":null},{"url":null,"slug":"conditional-generation-of-synthetic","title":"Conditional Generation of Synthetic Geospatial Images from Pixel-level and Feature-level Inputs","date":"2021-09-11","arxiv_id":"2109.05201","repositories_listed":0,"syntology":null},{"url":null,"slug":"hintedbt-augmenting-back-translation-with","title":"HintedBT: Augmenting Back-Translation with Quality and Transliteration Hints","date":"2021-09-09","arxiv_id":"2109.04443","repositories_listed":0,"syntology":null},{"url":null,"slug":"sanitais-unsupervised-data-augmentation-to","title":"SanitAIs: Unsupervised Data Augmentation to Sanitize Trojaned Neural Networks","date":"2021-09-09","arxiv_id":"2109.04566","repositories_listed":0,"syntology":null},{"url":null,"slug":"translate-fill-improving-zero-shot","title":"Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data","date":"2021-09-09","arxiv_id":"2109.04319","repositories_listed":0,"syntology":null},{"url":null,"slug":"crnntl-convolutional-recurrent-neural-network","title":"CRNNTL: convolutional recurrent neural network and transfer learning for QSAR modelling","date":"2021-09-07","arxiv_id":"2109.03309","repositories_listed":0,"syntology":null},{"url":null,"slug":"ganser-a-self-supervised-data-augmentation","title":"GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition","date":"2021-09-07","arxiv_id":"2109.03124","repositories_listed":0,"syntology":null},{"url":null,"slug":"generatively-augmented-neural-network","title":"Generatively Augmented Neural Network Watchdog for Image Classification Networks","date":"2021-09-07","arxiv_id":"2109.06168","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-tumor-segmentation-through","title":"Self-supervised Tumor Segmentation through Layer Decomposition","date":"2021-09-07","arxiv_id":"2109.03230","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-convolutional-neural-networks-1","title":"Evaluation of Convolutional Neural Networks for COVID-19 Classification on Chest X-Rays","date":"2021-09-06","arxiv_id":"2109.02415","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-person-generation-a-survey-from-the","title":"Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis","date":"2021-09-05","arxiv_id":"2109.02081","repositories_listed":0,"syntology":null},{"url":null,"slug":"sensor-data-augmentation-with-resampling-for","title":"Sensor Data Augmentation by Resampling for Contrastive Learning in Human Activity Recognition","date":"2021-09-05","arxiv_id":"2109.02054","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-mitosis-detection-using-a-cascade-mask","title":"Robust Mitosis Detection Using a Cascade Mask-RCNN Approach With Domain-Specific Residual Cycle-GAN Data Augmentation","date":"2021-09-04","arxiv_id":"2109.01878","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-detection-of-contextual","title":"Self-Supervised Detection of Contextual Synonyms in a Multi-Class Setting: Phenotype Annotation Use Case","date":"2021-09-04","arxiv_id":"2109.01935","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-from-multiple-noisy-augmented-data","title":"Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding","date":"2021-09-03","arxiv_id":"2109.01583","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-models-for-multi-illumination","title":"Generative Models for Multi-Illumination Color Constancy","date":"2021-09-02","arxiv_id":"2109.00863","repositories_listed":0,"syntology":null},{"url":null,"slug":"mitodet-simple-and-robust-mitosis-detection","title":"MitoDet: Simple and robust mitosis detection","date":"2021-09-02","arxiv_id":"2109.01485","repositories_listed":0,"syntology":null},{"url":null,"slug":"rotation-invariance-and-extensive-data","title":"Rotation Invariance and Extensive Data Augmentation: a strategy for the Mitosis Domain Generalization (MIDOG) Challenge","date":"2021-09-02","arxiv_id":"2109.00823","repositories_listed":0,"syntology":null},{"url":null,"slug":"application-of-deep-learning-methods-to-1","title":"Application of Deep Learning Methods to SNOMED CT Encoding of Clinical Texts: From Data Collection to Extreme Multi-Label Text-Based Classification","date":"2021-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"application-of-mix-up-method-in-document","title":"Application of Mix-Up Method in Document Classification Task Using BERT","date":"2021-09-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptive-cascade-r-cnn-for-mitosis","title":"Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge","date":"2021-09-01","arxiv_id":"2109.00965","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-mitosis-against-domain-shift-using","title":"Detecting Mitosis against Domain Shift using a Fused Detector and Deep Ensemble Classification Model for MIDOG Challenge","date":"2021-08-31","arxiv_id":"2108.13983","repositories_listed":0,"syntology":null},{"url":null,"slug":"maximum-f1-score-training-for-end-to-end","title":"Maximum F1-score training for end-to-end mispronunciation detection and diagnosis of L2 English speech","date":"2021-08-31","arxiv_id":"2108.13816","repositories_listed":0,"syntology":null}],"record_sha256":"9e4d6203e2e5eb05487e9b9819cb0ba985d7c5061df03126eb4e022535347eb8","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}