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Towards A Conceptually Simple Defensive Approach for Few-shot
  classifiers Against Adversarial Support Samples

Towards A Conceptually Simple Defensive Approach for Few-shot classifiers Against Adversarial Support Samples

24 October 2021
Y. Tan
Penny Chong
Jiamei Sun
Ngai-man Cheung
Yuval Elovici
Alexander Binder
    AAML
ArXivPDFHTML

Papers citing "Towards A Conceptually Simple Defensive Approach for Few-shot classifiers Against Adversarial Support Samples"

3 / 3 papers shown
Title
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve
  Adversarial Robustness
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness
Ahmadreza Jeddi
M. Shafiee
Michelle Karg
C. Scharfenberger
A. Wong
OOD
AAML
50
63
0
02 Mar 2020
Cross Attention Network for Few-shot Classification
Cross Attention Network for Few-shot Classification
Rui Hou
Hong Chang
Bingpeng Ma
Shiguang Shan
Xilin Chen
202
629
0
17 Oct 2019
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn
Pieter Abbeel
Sergey Levine
OOD
281
11,677
0
09 Mar 2017
1