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The shape and simplicity biases of adversarially robust ImageNet-trained CNNs
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The shape and simplicity biases of adversarially robust ImageNet-trained CNNs

Peijie Chen
Chirag Agarwal
Anh Totti Nguyen
    AAML
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Papers citing "The shape and simplicity biases of adversarially robust ImageNet-trained CNNs"

6 / 6 papers shown
Title
Shape Bias and Robustness Evaluation via Cue Decomposition for Image Classification and Segmentation
Shape Bias and Robustness Evaluation via Cue Decomposition for Image Classification and Segmentation
Edgar Heinert
Thomas Gottwald
Annika Mütze
Matthias Rottmann
147
0
0
16 Mar 2025
One Wave To Explain Them All: A Unifying Perspective On Feature Attribution
One Wave To Explain Them All: A Unifying Perspective On Feature Attribution
Gabriel Kasmi
Amandine Brunetto
Thomas Fel
Jayneel Parekh
AAMLFAtt
89
0
0
02 Oct 2024
On the Properties of Adversarially-Trained CNNs
On the Properties of Adversarially-Trained CNNs
Mattia Carletti
M. Terzi
Gian Antonio Susto
AAML
66
1
0
17 Mar 2022
Partial success in closing the gap between human and machine vision
Partial success in closing the gap between human and machine vision
Robert Geirhos
Kantharaju Narayanappa
Benjamin Mitzkus
Tizian Thieringer
Matthias Bethge
Felix Wichmann
Wieland Brendel
VLMAAML
107
230
0
14 Jun 2021
Do Input Gradients Highlight Discriminative Features?
Do Input Gradients Highlight Discriminative Features?
Harshay Shah
Prateek Jain
Praneeth Netrapalli
AAMLFAtt
120
59
0
25 Feb 2021
Out of Order: How Important Is The Sequential Order of Words in a Sentence in Natural Language Understanding Tasks?
Out of Order: How Important Is The Sequential Order of Words in a Sentence in Natural Language Understanding Tasks?
Thang M. Pham
Trung Bui
Long Mai
Anh Totti Nguyen
292
123
0
30 Dec 2020