GradAug: A New Regularization Method for Deep Neural Networks

14 June 2020

Papers citing "GradAug: A New Regularization Method for Deep Neural Networks"

9 / 9 papers shown

Title
FedUV: Uniformity and Variance for Heterogeneous Federated Learning Ha Min Son M. Kim Tai-Myung Chung Chao Huang Xin Liu FedML 43 3 0 27 Feb 2024
RoPGen: Towards Robust Code Authorship Attribution via Automatic Coding Style Transformation Zhen Li Guenevere Chen Chen Chen Chen Yayi Zou Shouhuai Xu AAML AI4TS 13 44 0 12 Feb 2022
Avoiding Overfitting: A Survey on Regularization Methods for Convolutional Neural Networks C. F. G. Santos João Paulo Papa 22 211 0 10 Jan 2022
A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap? Nathan G. Drenkow Numair Sani I. Shpitser Mathias Unberath 19 74 0 01 Dec 2021
Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning Matías Mendieta Taojiannan Yang Pu Wang Minwoo Lee Zhengming Ding C. L. P. Chen FedML 24 158 0 28 Nov 2021
Consistency-based Active Learning for Object Detection Weiping Yu Sijie Zhu Taojiannan Yang C. L. P. Chen ObjD 22 50 0 18 Mar 2021
Reflective-Net: Learning from Explanations Johannes Schneider Michalis Vlachos FAtt OffRL LRM 57 18 0 27 Nov 2020
Bag of Tricks for Image Classification with Convolutional Neural Networks Tong He Zhi-Li Zhang Hang Zhang Zhongyue Zhang Junyuan Xie Mu Li 221 1,399 0 04 Dec 2018
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results Antti Tarvainen Harri Valpola OOD MoMe 261 1,275 0 06 Mar 2017