A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective

21 August 2022

Papers citing "A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective"

11 / 61 papers shown

Title
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs T. Garipov Pavel Izmailov Dmitrii Podoprikhin Dmitry Vetrov A. Wilson UQCV 83 751 0 27 Feb 2018
Data Augmentation by Pairing Samples for Images Classification H. Inoue 160 422 0 09 Jan 2018
Between-class Learning for Image Classification Yuji Tokozume Yoshitaka Ushiku Tatsuya Harada SSL 71 205 0 28 Nov 2017
Learning from Between-class Examples for Deep Sound Recognition Yuji Tokozume Yoshitaka Ushiku Tatsuya Harada SSL 78 237 0 28 Nov 2017
mixup: Beyond Empirical Risk Minimization Hongyi Zhang Moustapha Cissé Yann N. Dauphin David Lopez-Paz NoLa 280 9,764 0 25 Oct 2017
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 427 2,941 0 15 Sep 2016
Wide Residual Networks Sergey Zagoruyko N. Komodakis 349 7,985 0 23 May 2016
Identity Mappings in Deep Residual Networks Kaiming He Xinming Zhang Shaoqing Ren Jian Sun 354 10,184 0 16 Mar 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 194,020 0 10 Dec 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 277 19,066 0 20 Dec 2014
Dropout Training as Adaptive Regularization Stefan Wager Sida I. Wang Percy Liang 129 599 0 04 Jul 2013