{"list":"other","total":80,"rows":[{"id":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"1512.00567","title":"Rethinking the Inception Architecture for Computer Vision","created":"2015-12-02","cited_by_count":5475},{"id":"1710.09412","title":"mixup: Beyond Empirical Risk Minimization","created":"2017-10-25","cited_by_count":5183},{"id":"1708.07747","title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","created":"2017-08-25","cited_by_count":4349},{"id":"1907.05600","title":"Generative Modeling by Estimating Gradients of the Data Distribution","created":"2019-07-12","cited_by_count":4101},{"id":"1606.03498","title":"Improved Techniques for Training GANs","created":"2016-06-10","cited_by_count":2970},{"id":"2204.03458","title":"Video Diffusion Models","created":"2022-04-07","cited_by_count":2305},{"id":"2001.07685","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","created":"2020-01-21","cited_by_count":2085},{"id":"1905.02249","title":"MixMatch: A Holistic Approach to Semi-Supervised Learning","created":"2019-05-06","cited_by_count":1391},{"id":"1904.12848","title":"Unsupervised Data Augmentation for Consistency Training","created":"2019-04-29","cited_by_count":1060},{"id":"1804.06872","title":"Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels","created":"2018-04-18","cited_by_count":1059},{"id":"1805.07836","title":"Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels","created":"2018-05-20","cited_by_count":997},{"id":"1704.03976","title":"Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning","created":"2017-04-13","cited_by_count":920},{"id":"2110.08263","title":"FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling","created":"2021-10-15","cited_by_count":540},{"id":"2006.05929","title":"Dataset Condensation with Gradient Matching","created":"2020-06-10","cited_by_count":516},{"id":"1908.06112","title":"Symmetric Cross Entropy for Robust Learning with Noisy Labels","created":"2019-08-16","cited_by_count":500},{"id":"2203.11932","title":"Dataset Distillation by Matching Training Trajectories","created":"2022-03-22","cited_by_count":442},{"id":"2002.09797","title":"Reliable Fidelity and Diversity Metrics for Generative Models","created":"2020-02-23","cited_by_count":434},{"id":"2007.00151","title":"Early-Learning Regularization Prevents Memorization of Noisy Labels","created":"2020-06-30","cited_by_count":416},{"id":"1901.04215","title":"How does Disagreement Help Generalization against Label Corruption?","created":"2019-01-14","cited_by_count":390},{"id":"2110.04181","title":"Dataset Condensation with Distribution Matching","created":"2021-10-08","cited_by_count":365},{"id":"2303.16203","title":"Your Diffusion Model is Secretly a Zero-Shot Classifier","created":"2023-03-28","cited_by_count":306},{"id":"2102.08259","title":"Dataset Condensation with Differentiable Siamese Augmentation","created":"2021-02-16","cited_by_count":291},{"id":"2003.02752","title":"Combating noisy labels by agreement: A joint training method with co-regularization","created":"2020-03-05","cited_by_count":289},{"id":"1708.02862","title":"WebVision Database: Visual Learning and Understanding from Web Data","created":"2017-08-09","cited_by_count":262},{"id":"2110.12088","title":"Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations","created":"2021-10-22","cited_by_count":256},{"id":"1802.05637","title":"cGANs with Projection Discriminator","created":"2018-02-15","cited_by_count":254},{"id":"2103.00550","title":"A Survey on Deep Semi-supervised Learning","created":"2021-02-28","cited_by_count":251},{"id":"1507.02672","title":"Semi-Supervised Learning with Ladder Networks","created":"2015-07-09","cited_by_count":247},{"id":"2205.07246","title":"FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning","created":"2022-05-15","cited_by_count":232},{"id":"1706.02613","title":"Decoupling \"when to update\" from \"how to update\"","created":"2017-06-08","cited_by_count":230},{"id":"2107.13034","title":"Dataset Distillation with Infinitely Wide Convolutional Networks","created":"2021-07-27","cited_by_count":218},{"id":"1903.07788","title":"Probabilistic End-to-end Noise Correction for Learning with Noisy Labels","created":"2019-03-19","cited_by_count":210},{"id":"2205.14959","title":"Dataset Condensation via Efficient Synthetic-Data Parameterization","created":"2022-05-30","cited_by_count":188},{"id":"2211.10586","title":"Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory","created":"2022-11-19","cited_by_count":181},{"id":"2206.00719","title":"Dataset Distillation using Neural Feature Regression","created":"2022-06-01","cited_by_count":177},{"id":"2011.11183","title":"CoMatch: Semi-supervised Learning with Contrastive Graph Regularization","created":"2020-11-23","cited_by_count":167},{"id":"2109.00650","title":"Dash: Semi-Supervised Learning with Dynamic Thresholding","created":"2021-09-01","cited_by_count":148},{"id":"2006.07805","title":"Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning","created":"2020-06-14","cited_by_count":142},{"id":"2306.13092","title":"Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective","created":"2023-06-22","cited_by_count":138},{"id":"2301.10921","title":"SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning","created":"2023-01-26","cited_by_count":133},{"id":"2011.04406","title":"A Survey of Label-noise Representation Learning: Past, Present and Future","created":"2020-11-09","cited_by_count":122},{"id":"2203.06915","title":"SimMatch: Semi-supervised Learning with Similarity Matching","created":"2022-03-14","cited_by_count":118},{"id":"2310.05773","title":"Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching","created":"2023-10-09","cited_by_count":118},{"id":"2210.16774","title":"Dataset Distillation via Factorization","created":"2022-10-30","cited_by_count":115},{"id":"2210.12067","title":"Efficient Dataset Distillation Using Random Feature Approximation","created":"2022-10-21","cited_by_count":111},{"id":"2312.03526","title":"On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm","created":"2023-12-06","cited_by_count":109},{"id":"2202.02916","title":"Dataset Condensation with Contrastive Signals","created":"2022-02-07","cited_by_count":105},{"id":"2002.08053","title":"Progressive Identification of True Labels for Partial-Label Learning","created":"2020-02-19","cited_by_count":102},{"id":"2102.02400","title":"Provably End-to-end Label-Noise Learning without Anchor Points","created":"2021-02-04","cited_by_count":97},{"id":"2208.07204","title":"USB: A Unified Semi-supervised Learning Benchmark for Classification","created":"2022-08-12","cited_by_count":95},{"id":"2007.08929","title":"Provably Consistent Partial-Label Learning","created":"2020-07-17","cited_by_count":92},{"id":"2311.16845","title":"Wavelet-based Fourier Information Interaction with Frequency Diffusion Adjustment for Underwater Image Restoration","created":"2023-11-28","cited_by_count":88},{"id":"2310.00093","title":"DataDAM: Efficient Dataset Distillation with Attention Matching","created":"2023-09-29","cited_by_count":84},{"id":"2305.15241","title":"Robust Classification via a Single Diffusion Model","created":"2023-05-24","cited_by_count":79},{"id":"2311.11638","title":"Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Model","created":"2023-11-20","cited_by_count":69},{"id":"2106.05731","title":"Leveraged Weighted Loss for Partial Label Learning","created":"2021-06-10","cited_by_count":66},{"id":"2203.14206","title":"Denoising Likelihood Score Matching for Conditional Score-based Data Generation","created":"2022-03-27","cited_by_count":46},{"id":"2404.13733","title":"Elucidating the Design Space of Dataset Condensation","created":"2024-04-21","cited_by_count":40},{"id":"2307.00467","title":"MissDiff: Training Diffusion Models on Tabular Data with Missing Values","created":"2023-07-02","cited_by_count":39},{"id":"2209.10365","title":"SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning","created":"2022-09-21","cited_by_count":31},{"id":"2004.06316","title":"Learning from Aggregate Observations","created":"2020-04-14","cited_by_count":30},{"id":"2311.08819","title":"Frequency Domain-based Dataset Distillation","created":"2023-11-15","cited_by_count":28},{"id":"2311.18838","title":"Dataset Distillation via Curriculum Data Synthesis in Large Data Era","created":"2023-11-30","cited_by_count":26},{"id":"2402.02316","title":"Your Diffusion Model is Secretly a Certifiably Robust Classifier","created":"2024-02-04","cited_by_count":24},{"id":"2206.00830","title":"Progressive Purification for Instance-Dependent Partial Label Learning","created":"2022-06-02","cited_by_count":23},{"id":"1811.11165","title":"Label-Noise Robust Generative Adversarial Networks","created":"2018-11-27","cited_by_count":22},{"id":"2201.08984","title":"PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning","created":"2022-01-22","cited_by_count":21},{"id":"2403.06075","title":"Multisize Dataset Condensation","created":"2024-03-10","cited_by_count":19},{"id":"2403.10045","title":"Towards Adversarially Robust Dataset Distillation by Curvature Regularization","created":"2024-03-15","cited_by_count":17},{"id":"2502.10184","title":"Realistic Evaluation of Deep Partial-Label Learning Algorithms","created":"2025-02-14","cited_by_count":12},{"id":"2305.14258","title":"Weakly Supervised AUC Optimization: A Unified Partial AUC Approach","created":"2023-05-23","cited_by_count":11},{"id":"2309.14183","title":"Species196: A One-Million Semi-supervised Dataset for Fine-grained Species Recognition","created":"2023-09-25","cited_by_count":11},{"id":"2305.12715","title":"Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations","created":"2023-05-22","cited_by_count":9},{"id":"2311.13718","title":"A Unified Approach to Count-Based Weakly-Supervised Learning","created":"2023-11-22","cited_by_count":9},{"id":"2506.00557","title":"Score Matching With Missing Data","created":"2025-05-31","cited_by_count":8},{"id":"2305.08295","title":"CLImage: Human-Annotated Datasets for Complementary-Label Learning","created":"2023-05-15","cited_by_count":7},{"id":"2310.00900","title":"uSee: Unified Speech Enhancement and Editing with Conditional Diffusion Models","created":"2023-10-02","cited_by_count":7},{"id":"2306.11343","title":"A Universal Unbiased Method for Classification from Aggregate Observations","created":"2023-06-20","cited_by_count":5},{"id":"2503.03789","title":"Positive-Unlabeled Diffusion Models for Preventing Sensitive Data Generation","created":"2025-03-05","cited_by_count":2}],"next_cursor":null}