{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/mixup/papers/5","list_of":"/method/mixup","method":"Mixup","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":5,"pages_in_order":7,"rows_per_page":100,"rows":[401,500],"of":651,"counts":{"archive_papers_tagged":651,"with_a_code_link":308,"where_syntology_ran_a_sample":89,"not_listed_spam_title":0,"listed":651,"listed_where_code_ran":89,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":74,"every_run_a_failure_of_syntologys_instrument":15,"listed_with_a_run_with_no_instrument_failure":74,"listed_every_run_a_failure_of_syntologys_instrument":15,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/mixup","prev":"/method/mixup/papers/4","next":"/method/mixup/papers/6","papers":[{"paper":"/paper/multi-view-consistent-generative-adversarial","slug":"multi-view-consistent-generative-adversarial","title":"Multi-View Consistent Generative Adversarial Networks for 3D-aware Image Synthesis","date":"2022-04-13","arxiv_id":"2204.06307","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":{"repos":["Xuanmeng-Zhang/MVCGAN"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":"/paper/characterizing-and-understanding-the-behavior","slug":"characterizing-and-understanding-the-behavior","title":"Characterizing and Understanding the Behavior of Quantized Models for Reliable Deployment","date":"2022-04-08","arxiv_id":"2204.04220","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-sample-z-mixup-richer-more-realistic","title":"Multi-Sample $ζ$-mixup: Richer, More Realistic Synthetic Samples from a $p$-Series Interpolant","date":"2022-04-07","arxiv_id":"2204.03323","n_code_links":0,"syntology":null},{"paper":"/paper/co-teaching-for-unsupervised-domain","slug":"co-teaching-for-unsupervised-domain","title":"Co-Teaching for Unsupervised Domain Adaptation and Expansion","date":"2022-04-04","arxiv_id":"2204.01210","n_code_links":1,"syntology":null},{"paper":"/paper/pp-yoloe-an-evolved-version-of-yolo","slug":"pp-yoloe-an-evolved-version-of-yolo","title":"PP-YOLOE: An evolved version of YOLO","date":"2022-03-30","arxiv_id":"2203.16250","n_code_links":8,"syntology":{"ran":22,"of":27,"n_ran_checked":21,"n_instrument":1,"unverified":5,"pointer_only":1,"phrase":"22 ran (of which 0 constructed an object rather than computing a result; 21 with no instrument failure: 2 honoured, 0 violated, 19 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","official":{"repos":["PaddlePaddle/PaddleDetection"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/leveraging-expert-guided-adversarial","slug":"leveraging-expert-guided-adversarial","title":"Leveraging Expert Guided Adversarial Augmentation For Improving Generalization in Named Entity Recognition","date":"2022-03-21","arxiv_id":"2203.10693","n_code_links":1,"syntology":null},{"paper":"/paper/stemm-self-learning-with-speech-text-manifold","slug":"stemm-self-learning-with-speech-text-manifold","title":"STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation","date":"2022-03-20","arxiv_id":"2203.10426","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ictnlp/stemm"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"on-the-calibration-of-pre-trained-language","title":"On the Calibration of Pre-trained Language Models using Mixup Guided by Area Under the Margin and Saliency","date":"2022-03-14","arxiv_id":"2203.07559","n_code_links":0,"syntology":null},{"paper":null,"slug":"lesionpaste-one-shot-anomaly-detection-for","title":"LesionPaste: One-Shot Anomaly Detection for Medical Images","date":"2022-03-12","arxiv_id":"2203.06354","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-study-on-joint-modeling-and-data","title":"A study on joint modeling and data augmentation of multi-modalities for audio-visual scene classification","date":"2022-03-07","arxiv_id":"2203.04114","n_code_links":0,"syntology":null},{"paper":"/paper/cyclemix-a-holistic-strategy-for-medical","slug":"cyclemix-a-holistic-strategy-for-medical","title":"CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision","date":"2022-03-03","arxiv_id":"2203.01475","n_code_links":1,"syntology":null},{"paper":null,"slug":"modality-adaptive-mixup-and-invariant","title":"Modality-Adaptive Mixup and Invariant Decomposition for RGB-Infrared Person Re-Identification","date":"2022-03-03","arxiv_id":"2203.01735","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-non-native-word-level-pronunciation","title":"Improving Non-native Word-level Pronunciation Scoring with Phone-level Mixup Data Augmentation and Multi-source Information","date":"2022-03-01","arxiv_id":"2203.01826","n_code_links":0,"syntology":null},{"paper":null,"slug":"background-mixup-data-augmentation-for-hand","title":"Background Mixup Data Augmentation for Hand and Object-in-Contact Detection","date":"2022-02-28","arxiv_id":"2202.13941","n_code_links":0,"syntology":null},{"paper":null,"slug":"better-modelling-out-of-distribution","title":"Better Modelling Out-of-Distribution Regression on Distributed Acoustic Sensor Data Using Anchored Hidden State Mixup","date":"2022-02-23","arxiv_id":"2202.11283","n_code_links":0,"syntology":null},{"paper":null,"slug":"contrastive-mixup-learning-for-improved","title":"Contrastive-mixup learning for improved speaker verification","date":"2022-02-22","arxiv_id":"2202.10672","n_code_links":0,"syntology":null},{"paper":"/paper/g-mixup-graph-data-augmentation-for-graph","slug":"g-mixup-graph-data-augmentation-for-graph","title":"G-Mixup: Graph Data Augmentation for Graph Classification","date":"2022-02-15","arxiv_id":"2202.07179","n_code_links":1,"syntology":null},{"paper":"/paper/scorenet-learning-non-uniform-attention-and","slug":"scorenet-learning-non-uniform-attention-and","title":"ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification","date":"2022-02-15","arxiv_id":"2202.07570","n_code_links":1,"syntology":null},{"paper":"/paper/optimizing-random-mixup-with-gaussian","slug":"optimizing-random-mixup-with-gaussian","title":"NeuroMixGDP: A Neural Collapse-Inspired Random Mixup for Private Data Release","date":"2022-02-14","arxiv_id":"2202.06467","n_code_links":1,"syntology":null},{"paper":"/paper/low-confidence-samples-matter-for-domain","slug":"low-confidence-samples-matter-for-domain","title":"Low-confidence Samples Matter for Domain Adaptation","date":"2022-02-06","arxiv_id":"2202.02802","n_code_links":1,"syntology":null},{"paper":null,"slug":"domain-adaptation-via-bidirectional-cross","title":"Domain Adaptation via Bidirectional Cross-Attention Transformer","date":"2022-01-15","arxiv_id":"2201.05887","n_code_links":0,"syntology":null},{"paper":"/paper/preventing-manifold-intrusion-with-locality","slug":"preventing-manifold-intrusion-with-locality","title":"Preventing Manifold Intrusion with Locality: Local Mixup","date":"2022-01-12","arxiv_id":"2201.04368","n_code_links":1,"syntology":null},{"paper":null,"slug":"genlabel-mixup-relabeling-using-generative","title":"GenLabel: Mixup Relabeling using Generative Models","date":"2022-01-07","arxiv_id":"2201.02354","n_code_links":0,"syntology":null},{"paper":null,"slug":"linda-unsupervised-learning-to-interpolate-in-1","title":"LINDA: Unsupervised Learning to Interpolate in Natural Language Processing","date":"2021-12-28","arxiv_id":"2112.13969","n_code_links":0,"syntology":null},{"paper":null,"slug":"pose-adaptive-dual-mixup-for-few-shot-single","title":"Pose Adaptive Dual Mixup for Few-Shot Single-View 3D Reconstruction","date":"2021-12-23","arxiv_id":"2112.12484","n_code_links":0,"syntology":null},{"paper":null,"slug":"self-distillation-mixup-training-for-non","title":"Self-Distillation Mixup Training for Non-autoregressive Neural Machine Translation","date":"2021-12-22","arxiv_id":"2112.11640","n_code_links":0,"syntology":null},{"paper":null,"slug":"saliency-grafting-innocuous-attribution-1","title":"Saliency Grafting: Innocuous Attribution-Guided Mixup with Calibrated Label Mixing","date":"2021-12-16","arxiv_id":"2112.08796","n_code_links":0,"syntology":null},{"paper":null,"slug":"interpolated-joint-space-adversarial-training","title":"Interpolated Joint Space Adversarial Training for Robust and Generalizable Defenses","date":"2021-12-12","arxiv_id":"2112.06323","n_code_links":0,"syntology":null},{"paper":"/paper/multimodal-based-scene-aware-framework-for","slug":"multimodal-based-scene-aware-framework-for","title":"GUNNEL: Guided Mixup Augmentation and Multi-View Fusion for Aquatic Animal Segmentation","date":"2021-12-12","arxiv_id":"2112.06193","n_code_links":1,"syntology":null},{"paper":"/paper/visual-transformers-with-primal-object","slug":"visual-transformers-with-primal-object","title":"Visual Transformers with Primal Object Queries for Multi-Label Image Classification","date":"2021-12-10","arxiv_id":"2112.05485","n_code_links":1,"syntology":null},{"paper":null,"slug":"selectaugment-hierarchical-deterministic","title":"SelectAugment: Hierarchical Deterministic Sample Selection for Data Augmentation","date":"2021-12-06","arxiv_id":"2112.02862","n_code_links":0,"syntology":null},{"paper":"/paper/boosting-discriminative-visual-representation","slug":"boosting-discriminative-visual-representation","title":"Boosting Discriminative Visual Representation Learning with Scenario-Agnostic Mixup","date":"2021-11-30","arxiv_id":"2111.15454","n_code_links":1,"syntology":null},{"paper":"/paper/using-mixup-as-regularization-and-tuning","slug":"using-mixup-as-regularization-and-tuning","title":"Using mixup as regularization and tuning hyper-parameters for ResNets","date":"2021-11-23","arxiv_id":"2111.11616","n_code_links":1,"syntology":null},{"paper":"/paper/tyolov5-a-temporal-yolov5-detector-based-on","slug":"tyolov5-a-temporal-yolov5-detector-based-on","title":"TYolov5: A Temporal Yolov5 Detector Based on Quasi-Recurrent Neural Networks for Real-Time Handgun Detection in Video","date":"2021-11-17","arxiv_id":"2111.08867","n_code_links":1,"syntology":null},{"paper":null,"slug":"global-mixup-eliminating-ambiguity-with","title":"Global Mixup: Eliminating Ambiguity with Clustering Relationships","date":"2021-11-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"linda-unsupervised-learning-to-interpolate-in","title":"LINDA: Unsupervised Learning to Interpolate in Natural Language Processing","date":"2021-11-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"segmix-a-simple-structure-aware-data","title":"SegMix: A Simple Structure-Aware Data Augmentation Method","date":"2021-11-16","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"graph-transplant-node-saliency-guided-graph","title":"Graph Transplant: Node Saliency-Guided Graph Mixup with Local Structure Preservation","date":"2021-11-10","arxiv_id":"2111.05639","n_code_links":0,"syntology":null},{"paper":null,"slug":"mixacm-mixup-based-robustness-transfer-via","title":"MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps","date":"2021-11-09","arxiv_id":"2111.05073","n_code_links":0,"syntology":null},{"paper":"/paper/towards-calibrated-model-for-long-tailed","slug":"towards-calibrated-model-for-long-tailed","title":"Towards Calibrated Model for Long-Tailed Visual Recognition from Prior Perspective","date":"2021-11-06","arxiv_id":"2111.03874","n_code_links":1,"syntology":{"ran":6,"of":7,"n_ran_checked":6,"n_instrument":0,"unverified":1,"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) · 1 unverified","official":{"repos":["XuZhengzhuo/Prior-LT"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/lungattn-advanced-lung-sound-classification","slug":"lungattn-advanced-lung-sound-classification","title":"LungAttn: advanced lung sound classification using attention mechanism with dual TQWT and triple STFT spectrogram","date":"2021-10-29","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/on-the-effects-of-data-distortion-on-model","slug":"on-the-effects-of-data-distortion-on-model","title":"On the Effects of Artificial Data Modification","date":"2021-10-26","arxiv_id":"2110.13968","n_code_links":1,"syntology":null},{"paper":"/paper/rct-random-consistency-training-for-semi","slug":"rct-random-consistency-training-for-semi","title":"RCT: Random Consistency Training for Semi-supervised Sound Event Detection","date":"2021-10-21","arxiv_id":"2110.11144","n_code_links":2,"syntology":null},{"paper":null,"slug":"intrusion-free-graph-mixup-1","title":"ifMixup: Interpolating Graph Pair to Regularize Graph Classification","date":"2021-10-18","arxiv_id":"2110.09344","n_code_links":0,"syntology":null},{"paper":"/paper/towards-understanding-the-data-dependency-of-1","slug":"towards-understanding-the-data-dependency-of-1","title":"Towards Understanding the Data Dependency of Mixup-style Training","date":"2021-10-14","arxiv_id":"2110.07647","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":0,"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","official":{"repos":["2014mchidamb/mixup-data-dependency"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/rethinking-the-representational-continuity-1","slug":"rethinking-the-representational-continuity-1","title":"Representational Continuity for Unsupervised Continual Learning","date":"2021-10-13","arxiv_id":"2110.06976","n_code_links":1,"syntology":{"ran":4,"of":6,"n_ran_checked":3,"n_instrument":1,"unverified":2,"pointer_only":6,"phrase":"4 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["divyam3897/ucl"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/spatial-mixup-directional-loudness","slug":"spatial-mixup-directional-loudness","title":"Spatial mixup: Directional loudness modification as data augmentation for sound event localization and detection","date":"2021-10-12","arxiv_id":"2110.06126","n_code_links":1,"syntology":null},{"paper":null,"slug":"label-occurrence-balanced-mixup-for-long","title":"Label-Occurrence-Balanced Mixup for Long-tailed Recognition","date":"2021-10-11","arxiv_id":"2110.04964","n_code_links":0,"syntology":null},{"paper":null,"slug":"better-pseudo-label-joint-domain-aware-label","title":"Better Pseudo-label: Joint Domain-aware Label and Dual-classifier for Semi-supervised Domain Generalization","date":"2021-10-10","arxiv_id":"2110.04820","n_code_links":0,"syntology":null},{"paper":null,"slug":"operationalizing-convolutional-neural-network","title":"Operationalizing Convolutional Neural Network Architectures for Prohibited Object Detection in X-Ray Imagery","date":"2021-10-10","arxiv_id":"2110.04906","n_code_links":0,"syntology":null},{"paper":null,"slug":"observations-on-k-image-expansion-of-image","title":"Observations on K-image Expansion of Image-Mixing Augmentation for Classification","date":"2021-10-08","arxiv_id":"2110.04248","n_code_links":0,"syntology":null},{"paper":"/paper/noisy-feature-mixup","slug":"noisy-feature-mixup","title":"Noisy Feature Mixup","date":"2021-10-05","arxiv_id":"2110.02180","n_code_links":2,"syntology":{"ran":0,"of":3,"n_ran_checked":0,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"0 ran · 3 unverified","official":{"repos":["erichson/noisy_mixup"],"state":"official: harvested for another paper","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":[]}}},{"paper":null,"slug":"alignmix-improving-representations-by","title":"AlignMix: Improving representations by interpolating aligned features","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"confidence-score-weighting-adaptation-for","title":"Confidence Score Weighting Adaptation for Source-Free Unsupervised Domain Adaptation","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"dp-instahide-data-augmentations-provably","title":"DP-InstaHide: Data Augmentations Provably Enhance Guarantees Against Dataset Manipulations","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"from-biased-data-to-unbiased-models-a-meta","title":"From Biased Data to Unbiased Models: a Meta-Learning Approach","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"g-mixup-graph-augmentation-for-graph","title":"G-Mixup: Graph Augmentation for Graph Classification","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-discriminative-visual","title":"Improving Discriminative Visual Representation Learning via Automatic Mixup","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"intra-class-mixup-for-out-of-distribution","title":"Intra-class Mixup for Out-of-Distribution Detection","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"intrusion-free-graph-mixup","title":"Intrusion-Free Graph Mixup","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"k-mixup-regularization-for-deep-learning-via-1","title":"$k$-Mixup Regularization for Deep Learning via Optimal Transport","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"m-mix-generating-hard-negatives-via-multiple","title":"$m$-mix: Generating hard negatives via multiple samples mixing for contrastive learning","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"tailmix-overcoming-the-label-sparsity-for","title":"TailMix: Overcoming the Label Sparsity for Extreme Multi-label Classification","date":"2021-09-29","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":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,"n_code_links":0,"syntology":null},{"paper":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","n_code_links":0,"syntology":null},{"paper":"/paper/balanced-mixup-for-highly-imbalanced-medical","slug":"balanced-mixup-for-highly-imbalanced-medical","title":"Balanced-MixUp for Highly Imbalanced Medical Image Classification","date":"2021-09-20","arxiv_id":"2109.09850","n_code_links":1,"syntology":{"ran":6,"of":6,"n_ran_checked":1,"n_instrument":5,"unverified":0,"pointer_only":6,"phrase":"6 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; 5 where Syntology's instrument failed) · 0 unverified","official":{"repos":["agaldran/balanced_mixup"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"paper":"/paper/adversarial-mixing-policy-for-relaxing","slug":"adversarial-mixing-policy-for-relaxing","title":"Adversarial Mixing Policy for Relaxing Locally Linear Constraints in Mixup","date":"2021-09-15","arxiv_id":"2109.07177","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"pointer_only":1,"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","official":{"repos":["pai-smallisallyourneed/mixup-amp"],"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"]}}},{"paper":"/paper/learning-from-uneven-training-data-unlabeled","slug":"learning-from-uneven-training-data-unlabeled","title":"Learning with Different Amounts of Annotation: From Zero to Many Labels","date":"2021-09-09","arxiv_id":"2109.04408","n_code_links":1,"syntology":{"ran":2,"of":6,"n_ran_checked":0,"n_instrument":2,"unverified":4,"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) · 4 unverified","official":{"repos":["szhang42/uneven_training_data"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":4,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"mixup-decoding-for-diverse-machine","title":"Mixup Decoding for Diverse Machine Translation","date":"2021-09-08","arxiv_id":"2109.03402","n_code_links":0,"syntology":null},{"paper":"/paper/contrastive-mixup-self-and-semi-supervised","slug":"contrastive-mixup-self-and-semi-supervised","title":"Contrastive Mixup: Self- and Semi-Supervised learning for Tabular Domain","date":"2021-08-27","arxiv_id":"2108.12296","n_code_links":1,"syntology":null},{"paper":"/paper/recall-k-surrogate-loss-with-large-batches","slug":"recall-k-surrogate-loss-with-large-batches","title":"Recall@k Surrogate Loss with Large Batches and Similarity Mixup","date":"2021-08-25","arxiv_id":"2108.11179","n_code_links":2,"syntology":{"ran":8,"of":11,"n_ran_checked":8,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["yash0307/RecallatK_surrogate","yash0307/recallatk"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/geometry-aware-self-training-for-unsupervised","slug":"geometry-aware-self-training-for-unsupervised","title":"Geometry-Aware Self-Training for Unsupervised Domain Adaptationon Object Point Clouds","date":"2021-08-20","arxiv_id":"2108.09169","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"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","official":{"repos":["zou-longkun/gast"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/synface-face-recognition-with-synthetic-data","slug":"synface-face-recognition-with-synthetic-data","title":"SynFace: Face Recognition with Synthetic Data","date":"2021-08-18","arxiv_id":"2108.07960","n_code_links":1,"syntology":null},{"paper":"/paper/carvemix-a-simple-data-augmentation-method","slug":"carvemix-a-simple-data-augmentation-method","title":"CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation","date":"2021-08-16","arxiv_id":"2108.06883","n_code_links":1,"syntology":null},{"paper":"/paper/context-aware-mixup-for-domain-adaptive","slug":"context-aware-mixup-for-domain-adaptive","title":"Context-Aware Mixup for Domain Adaptive Semantic Segmentation","date":"2021-08-08","arxiv_id":"2108.03557","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":2,"n_instrument":1,"unverified":1,"pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["qianyuzqy/CAMix"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"paper":null,"slug":"improving-distinction-between-asr-errors-and","title":"Improving Distinction between ASR Errors and Speech Disfluencies with Feature Space Interpolation","date":"2021-08-04","arxiv_id":"2108.01812","n_code_links":0,"syntology":null},{"paper":null,"slug":"continental-scale-building-detection-from","title":"Continental-Scale Building Detection from High Resolution Satellite Imagery","date":"2021-07-26","arxiv_id":"2107.12283","n_code_links":0,"syntology":null},{"paper":"/paper/meta-fdmixup-cross-domain-few-shot-learning","slug":"meta-fdmixup-cross-domain-few-shot-learning","title":"Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data","date":"2021-07-26","arxiv_id":"2107.11978","n_code_links":1,"syntology":null},{"paper":null,"slug":"structure-destruction-and-content-combination","title":"Structure Destruction and Content Combination for Face Anti-Spoofing","date":"2021-07-22","arxiv_id":"2107.10628","n_code_links":0,"syntology":null},{"paper":null,"slug":"uncertainty-guided-mixup-for-semi-supervised","title":"Uncertainty-Guided Mixup for Semi-Supervised Domain Adaptation without Source Data","date":"2021-07-14","arxiv_id":"2107.06707","n_code_links":0,"syntology":null},{"paper":"/paper/fedmix-approximation-of-mixup-under-mean-1","slug":"fedmix-approximation-of-mixup-under-mean-1","title":"FedMix: Approximation of Mixup under Mean Augmented Federated Learning","date":"2021-07-01","arxiv_id":"2107.00233","n_code_links":1,"syntology":null},{"paper":null,"slug":"segmenting-3d-hybrid-scenes-via-zero-shot","title":"Language-Level Semantics Conditioned 3D Point Cloud Segmentation","date":"2021-07-01","arxiv_id":"2107.00430","n_code_links":0,"syntology":null},{"paper":null,"slug":"generalization-of-reinforcement-learning-with","title":"Generalization of Reinforcement Learning with Policy-Aware Adversarial Data Augmentation","date":"2021-06-29","arxiv_id":"2106.15587","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-theory-driven-self-labeling-refinement","title":"A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning","date":"2021-06-28","arxiv_id":"2106.14749","n_code_links":0,"syntology":null},{"paper":null,"slug":"affinity-mixup-for-weakly-supervised-sound","title":"Affinity Mixup for Weakly Supervised Sound Event Detection","date":"2021-06-21","arxiv_id":"2106.11233","n_code_links":0,"syntology":null},{"paper":null,"slug":"graphmixup-improving-class-imbalanced-node","title":"GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction","date":"2021-06-21","arxiv_id":"2106.11133","n_code_links":0,"syntology":null},{"paper":"/paper/stylemix-separating-content-and-style-for","slug":"stylemix-separating-content-and-style-for","title":"StyleMix: Separating Content and Style for Enhanced Data Augmentation","date":"2021-06-19","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"synthesize-it-classifier-learning-a-1","title":"Synthesize-It-Classifier: Learning a Generative Classifier Through Recurrent Self-Analysis","date":"2021-06-19","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-dark-side-of-calibration-for-modern","title":"On Deep Neural Network Calibration by Regularization and its Impact on Refinement","date":"2021-06-17","arxiv_id":"2106.09385","n_code_links":0,"syntology":null},{"paper":"/paper/ssmix-saliency-based-span-mixup-for-text","slug":"ssmix-saliency-based-span-mixup-for-text","title":"SSMix: Saliency-Based Span Mixup for Text Classification","date":"2021-06-15","arxiv_id":"2106.08062","n_code_links":1,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"0 ran · 2 unverified","official":{"repos":["clovaai/ssmix"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"paper":"/paper/vision-language-navigation-with-random","slug":"vision-language-navigation-with-random","title":"Vision-Language Navigation with Random Environmental Mixup","date":"2021-06-15","arxiv_id":"2106.07876","n_code_links":1,"syntology":{"ran":1,"of":4,"n_ran_checked":0,"n_instrument":1,"unverified":3,"pointer_only":0,"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) · 3 unverified","official":{"repos":["lcfractal/vlnrem"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"coded-invnet-for-resilient-prediction-serving","title":"Coded-InvNet for Resilient Prediction Serving Systems","date":"2021-06-11","arxiv_id":"2106.06445","n_code_links":0,"syntology":null},{"paper":"/paper/fine-grained-out-of-distribution-detection","slug":"fine-grained-out-of-distribution-detection","title":"Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained Environments","date":"2021-06-07","arxiv_id":"2106.03917","n_code_links":1,"syntology":null},{"paper":null,"slug":"mixrl-data-mixing-augmentation-for-regression","title":"RegMix: Data Mixing Augmentation for Regression","date":"2021-06-07","arxiv_id":"2106.03374","n_code_links":0,"syntology":null},{"paper":"/paper/k-mixup-regularization-for-deep-learning-via","slug":"k-mixup-regularization-for-deep-learning-via","title":"k-Mixup Regularization for Deep Learning via Optimal Transport","date":"2021-06-05","arxiv_id":"2106.02933","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":1,"n_instrument":2,"unverified":1,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":{"repos":["anminggu/kmixup-cifar10"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/saint-improved-neural-networks-for-tabular","slug":"saint-improved-neural-networks-for-tabular","title":"SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training","date":"2021-06-02","arxiv_id":"2106.01342","n_code_links":7,"syntology":{"ran":19,"of":26,"n_ran_checked":19,"n_instrument":0,"unverified":7,"pointer_only":13,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 19 with no instrument failure: 1 honoured, 2 violated, 16 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","official":{"repos":["somepago/saint"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/mixup-for-node-and-graph-classification","slug":"mixup-for-node-and-graph-classification","title":"Mixup for Node and Graph Classification","date":"2021-06-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"two-stage-training-for-learning-from-label","title":"Two-stage Training for Learning from Label Proportions","date":"2021-05-22","arxiv_id":"2105.10635","n_code_links":0,"syntology":null},{"paper":null,"slug":"county-aggregation-mixup-augmentation-courage","title":"COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 Prediction","date":"2021-05-03","arxiv_id":"2105.00620","n_code_links":0,"syntology":null},{"paper":null,"slug":"airmixml-over-the-air-data-mixup-for","title":"AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning","date":"2021-05-02","arxiv_id":"2105.00395","n_code_links":0,"syntology":null}],"record_sha256":"64f1ea5ffa28117e0ff7eabfa3322922aaf56c037856bc70e64ca9b270b604d7","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}