{"list":"other","total":69,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"1503.02531","title":"Distilling the Knowledge in a Neural Network","created":"2015-03-09","cited_by_count":10324},{"id":"1608.06993","title":"Densely Connected Convolutional Networks","created":"2016-08-25","cited_by_count":6720},{"id":"1512.00567","title":"Rethinking the Inception Architecture for Computer Vision","created":"2015-12-02","cited_by_count":5475},{"id":"2006.05525","title":"Knowledge Distillation: A Survey","created":"2020-06-09","cited_by_count":1603},{"id":"2007.01434","title":"In Search of Lost Domain Generalization","created":"2020-07-02","cited_by_count":1054},{"id":"1906.02629","title":"When Does Label Smoothing Help?","created":"2019-06-06","cited_by_count":988},{"id":"1904.05068","title":"Relational Knowledge Distillation","created":"2019-04-10","cited_by_count":889},{"id":"1712.06541","title":"Size-Independent Sample Complexity of Neural Networks","created":"2017-12-18","cited_by_count":290},{"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":"1605.07723","title":"Data Programming: Creating Large Training Sets, Quickly","created":"2016-05-25","cited_by_count":246},{"id":"1703.00593","title":"Positive-Unlabeled Learning with Non-Negative Risk Estimator","created":"2017-03-02","cited_by_count":219},{"id":"1802.05300","title":"Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise","created":"2018-02-14","cited_by_count":216},{"id":"2003.02819","title":"Does label smoothing mitigate label noise?","created":"2020-03-05","cited_by_count":204},{"id":"2010.00467","title":"Bag of Tricks for Adversarial Training","created":"2020-10-01","cited_by_count":200},{"id":"1912.02911","title":"Deep learning with noisy labels: exploring techniques and remedies in medical image analysis","created":"2019-12-05","cited_by_count":173},{"id":"2105.13093","title":"Towards Understanding Knowledge Distillation","created":"2021-05-27","cited_by_count":158},{"id":"1811.12808","title":"Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning","created":"2018-11-13","cited_by_count":157},{"id":"1908.07387","title":"NLNL: Negative Learning for Noisy Labels","created":"2019-08-19","cited_by_count":151},{"id":"2002.08053","title":"Progressive Identification of True Labels for Partial-Label Learning","created":"2020-02-19","cited_by_count":102},{"id":"1711.09535","title":"Learning with Biased Complementary Labels","created":"2017-11-27","cited_by_count":93},{"id":"2007.08929","title":"Provably Consistent Partial-Label Learning","created":"2020-07-17","cited_by_count":92},{"id":"1705.07541","title":"Learning from Complementary Labels","created":"2017-05-22","cited_by_count":88},{"id":"1303.1208","title":"Classification with Asymmetric Label Noise: Consistency and Maximal Denoising","created":"2013-03-05","cited_by_count":68},{"id":"1603.02501","title":"Mixture Proportion Estimation via Kernel Embedding of Distributions","created":"2016-03-08","cited_by_count":67},{"id":"2106.05731","title":"Leveraged Weighted Loss for Partial Label Learning","created":"2021-06-10","cited_by_count":66},{"id":"1810.04327","title":"Complementary-Label Learning for Arbitrary Losses and Models","created":"2018-10-10","cited_by_count":57},{"id":"1912.12927","title":"Learning with Multiple Complementary Labels","created":"2019-12-30","cited_by_count":53},{"id":"2111.00980","title":"Mixture Proportion Estimation and PU Learning: A Modern Approach","created":"2021-11-01","cited_by_count":52},{"id":"2110.12911","title":"Instance-Dependent Partial Label Learning","created":"2021-10-25","cited_by_count":49},{"id":"2207.00810","title":"Eliciting and Learning with Soft Labels from Every Annotator","created":"2022-07-02","cited_by_count":43},{"id":"1702.04455","title":"Learning from Ambiguously Labeled Face Images","created":"2017-02-15","cited_by_count":29},{"id":"1910.08974","title":"Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach","created":"2019-10-20","cited_by_count":29},{"id":"2007.02235","title":"Unbiased Risk Estimators Can Mislead: A Case Study of Learning with Complementary Labels","created":"2020-07-05","cited_by_count":29},{"id":"1802.07427","title":"Active Learning with Partial Feedback","created":"2018-02-21","cited_by_count":27},{"id":"2202.00395","title":"Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification","created":"2022-02-01","cited_by_count":26},{"id":"2206.00830","title":"Progressive Purification for Instance-Dependent Partial Label Learning","created":"2022-06-02","cited_by_count":23},{"id":"1602.05307","title":"Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding","created":"2016-02-17","cited_by_count":22},{"id":"1904.02832","title":"A Regularization Approach for Instance-Based Superset Label Learning","created":"2019-04-05","cited_by_count":21},{"id":"2011.09172","title":"On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective","created":"2020-11-18","cited_by_count":21},{"id":"2201.08984","title":"PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning","created":"2022-01-22","cited_by_count":21},{"id":"2010.01875","title":"Pointwise Binary Classification with Pairwise Confidence Comparisons","created":"2020-10-05","cited_by_count":20},{"id":"2106.06152","title":"On the Robustness of Average Losses for Partial-Label Learning","created":"2021-06-11","cited_by_count":20},{"id":"2206.09672","title":"Adaptive Domain Interest Network for Multi-domain Recommendation","created":"2022-06-20","cited_by_count":20},{"id":"2102.06879","title":"Learning from Similarity-Confidence Data","created":"2021-02-13","cited_by_count":19},{"id":"2002.03673","title":"Rethinking Class-Prior Estimation for Positive-Unlabeled Learning","created":"2020-02-10","cited_by_count":15},{"id":"2301.06013","title":"Rethinking Precision of Pseudo Label: Test-Time Adaptation via Complementary Learning","created":"2023-01-15","cited_by_count":14},{"id":"1910.09388","title":"An Unbiased Risk Estimator for Learning with Augmented Classes","created":"2019-10-21","cited_by_count":13},{"id":"2310.05632","title":"Binary Classification with Confidence Difference","created":"2023-10-09","cited_by_count":13},{"id":"2211.01407","title":"On the Informativeness of Supervision Signals","created":"2022-11-02","cited_by_count":12},{"id":"2502.10184","title":"Realistic Evaluation of Deep Partial-Label Learning Algorithms","created":"2025-02-14","cited_by_count":12},{"id":"1911.05166","title":"Negative sampling in semi-supervised learning","created":"2019-11-12","cited_by_count":11},{"id":"2209.13777","title":"An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning","created":"2022-09-28","cited_by_count":10},{"id":"2305.16912","title":"Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning","created":"2023-05-26","cited_by_count":10},{"id":"2311.15502","title":"Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical","created":"2023-11-27","cited_by_count":10},{"id":"2301.12077","title":"ALIM: Adjusting Label Importance Mechanism for Noisy Partial Label Learning","created":"2023-01-28","cited_by_count":9},{"id":"2001.10642","title":"Binary Classification from Positive Data with Skewed Confidence","created":"2020-01-29","cited_by_count":8},{"id":"2112.12303","title":"Learning with Proper Partial Labels","created":"2021-12-23","cited_by_count":8},{"id":"2302.08155","title":"Learning From Biased Soft Labels","created":"2023-02-16","cited_by_count":8},{"id":"2209.09500","title":"Reduction from Complementary-Label Learning to Probability Estimates","created":"2022-09-20","cited_by_count":7},{"id":"2209.14189","title":"Class-Imbalanced Complementary-Label Learning via Weighted Loss","created":"2022-09-28","cited_by_count":7},{"id":"2305.08295","title":"CLImage: Human-Annotated Datasets for Complementary-Label Learning","created":"2023-05-15","cited_by_count":7},{"id":"1804.07759","title":"A Self-paced Regularization Framework for Partial-Label Learning","created":"2018-04-20","cited_by_count":6},{"id":"2008.01454","title":"Learning from a Complementary-label Source Domain: Theory and Algorithms","created":"2020-08-04","cited_by_count":6},{"id":"2212.06643","title":"Boosting Semi-Supervised Learning with Contrastive Complementary Labeling","created":"2022-12-13","cited_by_count":5},{"id":"2211.00269","title":"Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks","created":"2022-11-01","cited_by_count":4},{"id":"2510.04091","title":"Rethinking Consistent Multi-Label Classification Under Inexact Supervision","created":"2025-10-05","cited_by_count":3},{"id":"2509.24228","title":"Accessible, Realistic, and Fair Evaluation of Positive-Unlabeled Learning Algorithms","created":"2025-09-29","cited_by_count":1}],"next_cursor":null}