ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1906.06032
  4. Cited By
Adversarial Training Can Hurt Generalization

Adversarial Training Can Hurt Generalization

14 June 2019
Aditi Raghunathan
Sang Michael Xie
Fanny Yang
John C. Duchi
Percy Liang
ArXivPDFHTML

Papers citing "Adversarial Training Can Hurt Generalization"

16 / 66 papers shown
Title
OnlineAugment: Online Data Augmentation with Less Domain Knowledge
OnlineAugment: Online Data Augmentation with Less Domain Knowledge
Zhiqiang Tang
Yunhe Gao
Leonid Karlinsky
P. Sattigeri
Rogerio Feris
Dimitris N. Metaxas
19
56
0
17 Jul 2020
Do Adversarially Robust ImageNet Models Transfer Better?
Do Adversarially Robust ImageNet Models Transfer Better?
Hadi Salman
Andrew Ilyas
Logan Engstrom
Ashish Kapoor
A. Madry
37
419
0
16 Jul 2020
How benign is benign overfitting?
How benign is benign overfitting?
Amartya Sanyal
P. Dokania
Varun Kanade
Philip Torr
NoLa
AAML
25
57
0
08 Jul 2020
Provable tradeoffs in adversarially robust classification
Provable tradeoffs in adversarially robust classification
Yan Sun
Hamed Hassani
David Hong
Alexander Robey
23
53
0
09 Jun 2020
Feature Purification: How Adversarial Training Performs Robust Deep
  Learning
Feature Purification: How Adversarial Training Performs Robust Deep Learning
Zeyuan Allen-Zhu
Yuanzhi Li
MLT
AAML
39
148
0
20 May 2020
Increasing-Margin Adversarial (IMA) Training to Improve Adversarial
  Robustness of Neural Networks
Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks
Linhai Ma
Liang Liang
AAML
26
18
0
19 May 2020
Adversarial Training against Location-Optimized Adversarial Patches
Adversarial Training against Location-Optimized Adversarial Patches
Sukrut Rao
David Stutz
Bernt Schiele
AAML
19
92
0
05 May 2020
Adversarial Learning Guarantees for Linear Hypotheses and Neural
  Networks
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks
Pranjal Awasthi
Natalie Frank
M. Mohri
AAML
36
56
0
28 Apr 2020
Adversarial Training for Large Neural Language Models
Adversarial Training for Large Neural Language Models
Xiaodong Liu
Hao Cheng
Pengcheng He
Weizhu Chen
Yu-Chiang Frank Wang
Hoifung Poon
Jianfeng Gao
AAML
36
183
0
20 Apr 2020
No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT
  Scans by Augmenting with Adversarial Attacks
No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting with Adversarial Attacks
Siqi Liu
A. Setio
Florin-Cristian Ghesu
Eli Gibson
Sasa Grbic
Bogdan Georgescu
Dorin Comaniciu
AAML
OOD
33
40
0
08 Mar 2020
The Curious Case of Adversarially Robust Models: More Data Can Help,
  Double Descend, or Hurt Generalization
The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization
Yifei Min
Lin Chen
Amin Karbasi
AAML
39
69
0
25 Feb 2020
More Data Can Expand the Generalization Gap Between Adversarially Robust
  and Standard Models
More Data Can Expand the Generalization Gap Between Adversarially Robust and Standard Models
Lin Chen
Yifei Min
Mingrui Zhang
Amin Karbasi
OOD
38
64
0
11 Feb 2020
Improved Sample Complexities for Deep Networks and Robust Classification
  via an All-Layer Margin
Improved Sample Complexities for Deep Networks and Robust Classification via an All-Layer Margin
Colin Wei
Tengyu Ma
AAML
OOD
38
85
0
09 Oct 2019
Entropic Out-of-Distribution Detection
Entropic Out-of-Distribution Detection
David Macêdo
T. I. Ren
Cleber Zanchettin
Adriano Oliveira
Teresa B Ludermir
OODD
UQCV
25
32
0
15 Aug 2019
Invariance-inducing regularization using worst-case transformations
  suffices to boost accuracy and spatial robustness
Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness
Fanny Yang
Zuowen Wang
C. Heinze-Deml
28
42
0
26 Jun 2019
Robustness to Adversarial Perturbations in Learning from Incomplete Data
Robustness to Adversarial Perturbations in Learning from Incomplete Data
Amir Najafi
S. Maeda
Masanori Koyama
Takeru Miyato
OOD
32
129
0
24 May 2019
Previous
12