{"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/7","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":7,"pages_in_order":7,"rows_per_page":100,"rows":[601,651],"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/6","next":null,"papers":[{"paper":"/paper/long-tail-visual-relationship-recognition","slug":"long-tail-visual-relationship-recognition","title":"Exploring Long Tail Visual Relationship Recognition with Large Vocabulary","date":"2020-03-25","arxiv_id":"2004.00436","n_code_links":3,"syntology":{"ran":5,"of":5,"n_ran_checked":4,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["Vision-CAIR/LTVRR"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"bridge-the-domain-gap-between-ultra-wide","title":"Bridge the Domain Gap Between Ultra-wide-field and Traditional Fundus Images via Adversarial Domain Adaptation","date":"2020-03-23","arxiv_id":"2003.10042","n_code_links":0,"syntology":null},{"paper":"/paper/improving-calibration-in-mixup-trained-deep","slug":"improving-calibration-in-mixup-trained-deep","title":"On Calibration of Mixup Training for Deep Neural Networks","date":"2020-03-22","arxiv_id":"2003.09946","n_code_links":1,"syntology":null},{"paper":"/paper/roam-random-layer-mixup-for-semi-supervised","slug":"roam-random-layer-mixup-for-semi-supervised","title":"ROAM: Random Layer Mixup for Semi-Supervised Learning in Medical Imaging","date":"2020-03-20","arxiv_id":"2003.09439","n_code_links":1,"syntology":null},{"paper":null,"slug":"varmixup-exploiting-the-latent-space-for","title":"On the benefits of defining vicinal distributions in latent space","date":"2020-03-14","arxiv_id":"2003.06566","n_code_links":0,"syntology":null},{"paper":"/paper/adversarial-vertex-mixup-toward-better","slug":"adversarial-vertex-mixup-toward-better","title":"Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization","date":"2020-03-05","arxiv_id":"2003.02484","n_code_links":2,"syntology":null},{"paper":"/paper/understanding-and-enhancing-mixed-sample-data","slug":"understanding-and-enhancing-mixed-sample-data","title":"FMix: Enhancing Mixed Sample Data Augmentation","date":"2020-02-27","arxiv_id":"2002.12047","n_code_links":5,"syntology":{"ran":8,"of":8,"n_ran_checked":1,"n_instrument":7,"unverified":0,"pointer_only":3,"phrase":"8 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; 7 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ecs-vlc/FMix"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official","unlocated"]}}},{"paper":null,"slug":"calibrate-and-prune-improving-reliability-of","title":"Calibrate and Prune: Improving Reliability of Lottery Tickets Through Prediction Calibration","date":"2020-02-10","arxiv_id":"2002.03875","n_code_links":0,"syntology":null},{"paper":"/paper/batchboost-regularization-for-stabilizing","slug":"batchboost-regularization-for-stabilizing","title":"batchboost: regularization for stabilizing training with resistance to underfitting & overfitting","date":"2020-01-21","arxiv_id":"2001.07627","n_code_links":1,"syntology":null},{"paper":"/paper/compounding-the-performance-improvements-of","slug":"compounding-the-performance-improvements-of","title":"Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network","date":"2020-01-17","arxiv_id":"2001.06268","n_code_links":1,"syntology":{"ran":3,"of":11,"n_ran_checked":2,"n_instrument":1,"unverified":8,"pointer_only":0,"phrase":"3 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; 1 where Syntology's instrument failed) · 8 unverified","official":{"repos":["clovaai/assembled-cnn"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":8,"ran_from_kinds":["official"]}}},{"paper":"/paper/improve-unsupervised-domain-adaptation-with","slug":"improve-unsupervised-domain-adaptation-with","title":"Improve Unsupervised Domain Adaptation with Mixup Training","date":"2020-01-03","arxiv_id":"2001.00677","n_code_links":1,"syntology":null},{"paper":"/paper/large-scale-learning-of-general-visual","slug":"large-scale-learning-of-general-visual","title":"Big Transfer (BiT): General Visual Representation Learning","date":"2019-12-24","arxiv_id":"1912.11370","n_code_links":9,"syntology":{"ran":9,"of":10,"n_ran_checked":7,"n_instrument":2,"unverified":1,"pointer_only":0,"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) · 1 unverified","official":{"repos":["google-research/big_transfer"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"on-manifold-adversarial-data-augmentation","title":"On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration","date":"2019-12-16","arxiv_id":"1912.07458","n_code_links":0,"syntology":null},{"paper":"/paper/adversarial-domain-adaptation-with-domain","slug":"adversarial-domain-adaptation-with-domain","title":"Adversarial Domain Adaptation with Domain Mixup","date":"2019-12-04","arxiv_id":"1912.01805","n_code_links":1,"syntology":null},{"paper":null,"slug":"data-augmentation-for-deep-transfer-learning","title":"E-Stitchup: Data Augmentation for Pre-Trained Embeddings","date":"2019-11-28","arxiv_id":"1912.00772","n_code_links":0,"syntology":null},{"paper":"/paper/learning-spatial-fusion-for-single-shot","slug":"learning-spatial-fusion-for-single-shot","title":"Learning Spatial Fusion for Single-Shot Object Detection","date":"2019-11-21","arxiv_id":"1911.09516","n_code_links":1,"syntology":null},{"paper":null,"slug":"patch-level-neighborhood-interpolation-a","title":"Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy","date":"2019-11-21","arxiv_id":"1911.09307","n_code_links":0,"syntology":null},{"paper":"/paper/model-agnostic-approaches-to-handling-noisy","slug":"model-agnostic-approaches-to-handling-noisy","title":"Model-agnostic Approaches to Handling Noisy Labels When Training Sound Event Classifiers","date":"2019-10-26","arxiv_id":"1910.12004","n_code_links":1,"syntology":null},{"paper":"/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","n_code_links":1,"syntology":null},{"paper":"/paper/mixup-inference-better-exploiting-mixup-to","slug":"mixup-inference-better-exploiting-mixup-to","title":"Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks","date":"2019-09-25","arxiv_id":"1909.11515","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"2 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; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["P2333/Mixup-Inference"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"cross-corpus-data-augmentation-for-acoustic","title":"Cross-Corpus Data Augmentation for Acoustic Addressee Detection","date":"2019-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-based-strategy-for","title":"Deep Learning-Based Strategy for Macromolecules Classification with Imbalanced Data from Cellular Electron Cryotomography","date":"2019-08-27","arxiv_id":"1908.09993","n_code_links":0,"syntology":null},{"paper":null,"slug":"metamixup-learning-adaptive-interpolation","title":"MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning","date":"2019-08-27","arxiv_id":"1908.10059","n_code_links":0,"syntology":null},{"paper":"/paper/mish-a-self-regularized-non-monotonic-neural","slug":"mish-a-self-regularized-non-monotonic-neural","title":"Mish: A Self Regularized Non-Monotonic Activation Function","date":"2019-08-23","arxiv_id":"1908.08681","n_code_links":9,"syntology":{"ran":9,"of":12,"n_ran_checked":8,"n_instrument":1,"unverified":3,"pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["digantamisra98/Mish"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/improving-robustness-of-deep-learning-based","slug":"improving-robustness-of-deep-learning-based","title":"Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation","date":"2019-08-12","arxiv_id":"1908.04126","n_code_links":1,"syntology":null},{"paper":"/paper/pseudo-labeling-and-confirmation-bias-in-deep","slug":"pseudo-labeling-and-confirmation-bias-in-deep","title":"Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning","date":"2019-08-08","arxiv_id":"1908.02983","n_code_links":4,"syntology":{"ran":14,"of":20,"n_ran_checked":12,"n_instrument":2,"unverified":6,"pointer_only":3,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 2 where Syntology's instrument failed) · 6 unverified","official":null}},{"paper":"/paper/efficient-method-for-categorize-animals-in","slug":"efficient-method-for-categorize-animals-in","title":"Efficient Method for Categorize Animals in the Wild","date":"2019-07-30","arxiv_id":"1907.13037","n_code_links":1,"syntology":null},{"paper":"/paper/charting-the-right-manifold-manifold-mixup","slug":"charting-the-right-manifold-manifold-mixup","title":"Charting the Right Manifold: Manifold Mixup for Few-shot Learning","date":"2019-07-28","arxiv_id":"1907.12087","n_code_links":8,"syntology":null},{"paper":null,"slug":"mixup-of-feature-maps-in-a-hidden-layer-for","title":"Mixup of Feature Maps in a Hidden Layer for Training of Convolutional Neural Network","date":"2019-06-24","arxiv_id":"1906.09739","n_code_links":0,"syntology":null},{"paper":null,"slug":"data-interpolating-prediction-alternative","title":"Data Interpolating Prediction: Alternative Interpretation of Mixup","date":"2019-06-20","arxiv_id":"1906.08412","n_code_links":0,"syntology":null},{"paper":null,"slug":"mixup-as-directional-adversarial-training","title":"MixUp as Directional Adversarial Training","date":"2019-06-17","arxiv_id":"1906.06875","n_code_links":0,"syntology":null},{"paper":null,"slug":"190604809","title":"Suppressing Model Overfitting for Image Super-Resolution Networks","date":"2019-06-11","arxiv_id":"1906.04809","n_code_links":0,"syntology":null},{"paper":null,"slug":"retrieval-augmented-convolutional-neural-1","title":"Retrieval-Augmented Convolutional Neural Networks Against Adversarial Examples","date":"2019-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/on-mixup-training-improved-calibration-and","slug":"on-mixup-training-improved-calibration-and","title":"On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks","date":"2019-05-27","arxiv_id":"1905.11001","n_code_links":2,"syntology":null},{"paper":"/paper/augmenting-data-with-mixup-for-sentence","slug":"augmenting-data-with-mixup-for-sentence","title":"Augmenting Data with Mixup for Sentence Classification: An Empirical Study","date":"2019-05-22","arxiv_id":"1905.08941","n_code_links":3,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"segregation-network-for-multi-class-novelty","title":"Multi-class Novelty Detection Using Mix-up Technique","date":"2019-05-11","arxiv_id":"1905.04523","n_code_links":0,"syntology":null},{"paper":"/paper/virtual-mixup-training-for-unsupervised","slug":"virtual-mixup-training-for-unsupervised","title":"Virtual Mixup Training for Unsupervised Domain Adaptation","date":"2019-05-10","arxiv_id":"1905.04215","n_code_links":4,"syntology":null},{"paper":"/paper/manifold-mixup-learning-better","slug":"manifold-mixup-learning-better","title":"Manifold Mixup: Learning Better Representations by Interpolating Hidden States","date":"2019-05-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/unsupervised-label-noise-modeling-and-loss","slug":"unsupervised-label-noise-modeling-and-loss","title":"Unsupervised Label Noise Modeling and Loss Correction","date":"2019-04-25","arxiv_id":"1904.11238","n_code_links":2,"syntology":{"ran":3,"of":3,"n_ran_checked":2,"n_instrument":1,"unverified":0,"pointer_only":0,"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) · 0 unverified","official":null}},{"paper":"/paper/drop-an-octave-reducing-spatial-redundancy-in","slug":"drop-an-octave-reducing-spatial-redundancy-in","title":"Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution","date":"2019-04-10","arxiv_id":"1904.05049","n_code_links":28,"syntology":{"ran":24,"of":34,"n_ran_checked":11,"n_instrument":13,"unverified":10,"pointer_only":9,"phrase":"24 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 0 violated, 10 with no contract checked; 13 where Syntology's instrument failed) · 10 unverified","official":{"repos":["facebookresearch/OctConv"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/soft-conditional-computation","slug":"soft-conditional-computation","title":"CondConv: Conditionally Parameterized Convolutions for Efficient Inference","date":"2019-04-10","arxiv_id":"1904.04971","n_code_links":9,"syntology":null},{"paper":null,"slug":"kernel-free-image-deblurring-with-a-pair-of","title":"Blur Removal via Blurred-Noisy Image Pair","date":"2019-03-26","arxiv_id":"1903.10667","n_code_links":0,"syntology":null},{"paper":"/paper/manifold-mixup-improves-text-recognition-with","slug":"manifold-mixup-improves-text-recognition-with","title":"Manifold Mixup improves text recognition with CTC loss","date":"2019-03-11","arxiv_id":"1903.04246","n_code_links":1,"syntology":null},{"paper":"/paper/bag-of-tricks-for-image-classification-with","slug":"bag-of-tricks-for-image-classification-with","title":"Bag of Tricks for Image Classification with Convolutional Neural Networks","date":"2018-12-04","arxiv_id":"1812.01187","n_code_links":28,"syntology":{"ran":11,"of":15,"n_ran_checked":9,"n_instrument":2,"unverified":4,"pointer_only":5,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 2 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","official":{"repos":["dmlc/gluon-cv"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"generalization-bounds-for-vicinal-risk","title":"Generalization Bounds for Vicinal Risk Minimization Principle","date":"2018-11-11","arxiv_id":"1811.04351","n_code_links":0,"syntology":null},{"paper":null,"slug":"label-denoising-with-large-ensembles-of","title":"Label Denoising with Large Ensembles of Heterogeneous Neural Networks","date":"2018-09-12","arxiv_id":"1809.04403","n_code_links":0,"syntology":null},{"paper":"/paper/mixup-as-locally-linear-out-of-manifold","slug":"mixup-as-locally-linear-out-of-manifold","title":"MixUp as Locally Linear Out-Of-Manifold Regularization","date":"2018-09-07","arxiv_id":"1809.02499","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":2,"phrase":"2 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; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/manifold-mixup-better-representations-by","slug":"manifold-mixup-better-representations-by","title":"Manifold Mixup: Better Representations by Interpolating Hidden States","date":"2018-06-13","arxiv_id":"1806.05236","n_code_links":12,"syntology":{"ran":7,"of":11,"n_ran_checked":5,"n_instrument":2,"unverified":4,"pointer_only":5,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","official":{"repos":["vikasverma1077/manifold_mixup"],"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":["listed","unlocated"]}}},{"paper":null,"slug":"mixup-based-acoustic-scene-classification","title":"Mixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network","date":"2018-05-18","arxiv_id":"1805.07319","n_code_links":0,"syntology":null},{"paper":null,"slug":"retrieval-augmented-convolutional-neural","title":"Retrieval-Augmented Convolutional Neural Networks for Improved Robustness against Adversarial Examples","date":"2018-02-26","arxiv_id":"1802.09502","n_code_links":0,"syntology":null},{"paper":"/paper/mixup-beyond-empirical-risk-minimization","slug":"mixup-beyond-empirical-risk-minimization","title":"mixup: Beyond Empirical Risk Minimization","date":"2017-10-25","arxiv_id":"1710.09412","n_code_links":71,"syntology":{"ran":36,"of":47,"n_ran_checked":16,"n_instrument":20,"unverified":11,"pointer_only":15,"phrase":"36 ran (of which 5 constructed an object rather than computing a result; 16 with no instrument failure: 0 honoured, 0 violated, 16 with no contract checked; 20 where Syntology's instrument failed) · 11 unverified","official":{"repos":["facebookresearch/mixup-cifar10"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}}],"record_sha256":"a410d141e16e083ef8c1b5ef33892c3cc23e16a68b9a49e635eef8803079aed6","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}