Understanding deep learning requires rethinking generalization

10 November 2016

Benjamin Recht

Papers citing "Understanding deep learning requires rethinking generalization"

50 / 1,075 papers shown

Title
The Low-Rank Simplicity Bias in Deep Networks Minyoung Huh H. Mobahi Richard Y. Zhang Brian Cheung Pulkit Agrawal Phillip Isola 35 110 0 18 Mar 2021
Gradient Projection Memory for Continual Learning Gobinda Saha Isha Garg Kaushik Roy VLM CLL 47 270 0 17 Mar 2021
Triplet-Watershed for Hyperspectral Image Classification Aditya Challa Sravan Danda B. Sagar Laurent Najman 26 5 0 17 Mar 2021
Is it enough to optimize CNN architectures on ImageNet? Lukas Tuggener Jürgen Schmidhuber Thilo Stadelmann 33 23 0 16 Mar 2021
Detecting Human-Object Interaction via Fabricated Compositional Learning Zhi Hou B. Yu Yu Qiao Xiaojiang Peng Dacheng Tao 38 96 0 15 Mar 2021
Membership Inference Attacks on Machine Learning: A Survey Hongsheng Hu Z. Salcic Lichao Sun Gillian Dobbie Philip S. Yu Xuyun Zhang MIACV 40 412 0 14 Mar 2021
Intraclass clustering: an implicit learning ability that regularizes DNNs Simon Carbonnelle Christophe De Vleeschouwer 65 8 0 11 Mar 2021
Fair Mixup: Fairness via Interpolation Ching-Yao Chuang Youssef Mroueh 21 138 0 11 Mar 2021
Reframing Neural Networks: Deep Structure in Overcomplete Representations Calvin Murdock George Cazenavette Simon Lucey BDL 41 4 0 10 Mar 2021
LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment F. Cordeiro Ragav Sachdeva Vasileios Belagiannis Ian Reid G. Carneiro NoLa 21 77 0 06 Mar 2021
Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy Lucas Liebenwein Cenk Baykal Brandon Carter David K Gifford Daniela Rus AAML 42 71 0 04 Mar 2021
FSDR: Frequency Space Domain Randomization for Domain Generalization Jiaxing Huang Dayan Guan Aoran Xiao Shijian Lu 44 218 0 03 Mar 2021
Fixing Data Augmentation to Improve Adversarial Robustness Sylvestre-Alvise Rebuffi Sven Gowal D. A. Calian Florian Stimberg Olivia Wiles Timothy A. Mann AAML 36 271 0 02 Mar 2021
Posterior Meta-Replay for Continual Learning Christian Henning Maria R. Cervera Francesco DÁngelo J. Oswald Regina Traber Benjamin Ehret Seijin Kobayashi Benjamin Grewe João Sacramento CLL BDL 51 55 0 01 Mar 2021
DST: Data Selection and joint Training for Learning with Noisy Labels Yi Wei Xue Mei Xin Liu Pengxiang Xu NoLa 27 3 0 01 Mar 2021
Neuron Coverage-Guided Domain Generalization Chris Xing Tian Haoliang Li Xiaofei Xie Yang Liu Shiqi Wang 32 35 0 27 Feb 2021
FINE Samples for Learning with Noisy Labels Taehyeon Kim Jongwoo Ko Sangwook Cho J. Choi Se-Young Yun NoLa 38 103 0 23 Feb 2021
SVRG Meets AdaGrad: Painless Variance Reduction Benjamin Dubois-Taine Sharan Vaswani Reza Babanezhad Mark Schmidt Simon Lacoste-Julien 23 18 0 18 Feb 2021
Dissecting Supervised Contrastive Learning Florian Graf Christoph Hofer Marc Niethammer Roland Kwitt SSL 117 70 0 17 Feb 2021
Evaluating Multi-label Classifiers with Noisy Labels Wenting Zhao Carla P. Gomes NoLa 74 14 0 16 Feb 2021
Learning curves of generic features maps for realistic datasets with a teacher-student model Bruno Loureiro Cédric Gerbelot Hugo Cui Sebastian Goldt Florent Krzakala M. Mézard Lenka Zdeborová 37 136 0 16 Feb 2021
Training Larger Networks for Deep Reinforcement Learning Keita Ota Devesh K. Jha Asako Kanezaki OffRL 37 39 0 16 Feb 2021
Low Curvature Activations Reduce Overfitting in Adversarial Training Vasu Singla Sahil Singla David Jacobs S. Feizi AAML 43 45 0 15 Feb 2021
Learning by Turning: Neural Architecture Aware Optimisation Yang Liu Jeremy Bernstein M. Meister Yisong Yue ODL 48 26 0 14 Feb 2021
Technical Challenges for Training Fair Neural Networks Valeriia Cherepanova V. Nanda Micah Goldblum John P. Dickerson Tom Goldstein FaML 25 22 0 12 Feb 2021
Understanding the Interaction of Adversarial Training with Noisy Labels Jianing Zhu Jingfeng Zhang Bo Han Tongliang Liu Gang Niu Hongxia Yang Mohan Kankanhalli Masashi Sugiyama AAML 27 27 0 06 Feb 2021
On the Reproducibility of Neural Network Predictions Srinadh Bhojanapalli Kimberly Wilber Andreas Veit A. S. Rawat Seungyeon Kim A. Menon Sanjiv Kumar 29 35 0 05 Feb 2021
Provably End-to-end Label-Noise Learning without Anchor Points Xuefeng Li Tongliang Liu Bo Han Gang Niu Masashi Sugiyama NoLa 133 121 0 04 Feb 2021
SGD Generalizes Better Than GD (And Regularization Doesn't Help) I Zaghloul Amir Tomer Koren Roi Livni 37 46 0 01 Feb 2021
A Convergence Theory Towards Practical Over-parameterized Deep Neural Networks Asaf Noy Yi Tian Xu Y. Aflalo Lihi Zelnik-Manor Rong Jin 46 3 0 12 Jan 2021
Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning Milad Nasr Shuang Song Abhradeep Thakurta Nicolas Papernot Nicholas Carlini MIACV FedML 82 216 0 11 Jan 2021
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise Spencer Frei Yuan Cao Quanquan Gu FedML MLT 70 19 0 04 Jan 2021
A Second-Order Approach to Learning with Instance-Dependent Label Noise Zhaowei Zhu Tongliang Liu Yang Liu NoLa 22 127 0 22 Dec 2020
Neural Joint Entropy Estimation Yuval Shalev Amichai Painsky I. Ben-Gal 34 8 0 21 Dec 2020
Multi-Label Noise Robust Collaborative Learning for Remote Sensing Image Classification A. Aksoy Mahdyar Ravanbakhsh Begüm Demir 35 24 0 19 Dec 2020
Attentional-Biased Stochastic Gradient Descent Q. Qi Yi Tian Xu Rong Jin W. Yin Tianbao Yang ODL 31 12 0 13 Dec 2020
Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise Pengfei Chen Junjie Ye Guangyong Chen Jingwei Zhao Pheng-Ann Heng NoLa 45 123 0 10 Dec 2020
Annotation-efficient deep learning for automatic medical image segmentation Shanshan Wang Cheng Li Rongpin Wang Zaiyi Liu Meiyun Wang ... Xin Liu Jie Chen Hui-Chong Zhou Ismail Ben Ayed Hairong Zheng VLM MedIm 39 177 0 09 Dec 2020
A Topological Filter for Learning with Label Noise Pengxiang Wu Songzhu Zheng Mayank Goswami Dimitris N. Metaxas Chao Chen NoLa 30 112 0 09 Dec 2020
Multi-Objective Interpolation Training for Robustness to Label Noise Diego Ortego Eric Arazo Paul Albert Noel E. O'Connor Kevin McGuinness NoLa 33 112 0 08 Dec 2020
Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels Pengfei Chen Junjie Ye Guangyong Chen Jingwei Zhao Pheng-Ann Heng NoLa 103 34 0 08 Dec 2020
DiffPrune: Neural Network Pruning with Deterministic Approximate Binary Gates and $L_0$ Regularization Yaniv Shulman 46 3 0 07 Dec 2020
Privacy and Robustness in Federated Learning: Attacks and Defenses Lingjuan Lyu Han Yu Xingjun Ma Chen Chen Lichao Sun Jun Zhao Qiang Yang Philip S. Yu FedML 183 357 0 07 Dec 2020
Cross-Layer Distillation with Semantic Calibration Defang Chen Jian-Ping Mei Yuan Zhang Can Wang Yan Feng Chun-Yen Chen FedML 45 288 0 06 Dec 2020
SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning Zhuowei Wang Jing Jiang Bo Han Lei Feng Bo An Gang Niu Guodong Long NoLa 33 17 0 02 Dec 2020
Every Model Learned by Gradient Descent Is Approximately a Kernel Machine Pedro M. Domingos MLT 29 71 0 30 Nov 2020
Overcoming Measurement Inconsistency in Deep Learning for Linear Inverse Problems: Applications in Medical Imaging Marija Vella João F. C. Mota 19 4 0 29 Nov 2020
Rethinking Generalization in American Sign Language Prediction for Edge Devices with Extremely Low Memory Footprint A. Paul P. Mohan Stuti Sehgal 22 17 0 27 Nov 2020
Gradient Starvation: A Learning Proclivity in Neural Networks Mohammad Pezeshki Sekouba Kaba Yoshua Bengio Aaron Courville Doina Precup Guillaume Lajoie MLT 54 259 0 18 Nov 2020
Dynamic Hard Pruning of Neural Networks at the Edge of the Internet Lorenzo Valerio F. M. Nardini A. Passarella R. Perego 25 12 0 17 Nov 2020