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The shape and simplicity biases of adversarially robust ImageNet-trained
  CNNs
v1v2v3v4v5v6 (latest)

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

16 June 2020
Peijie Chen
Chirag Agarwal
Anh Totti Nguyen
    AAML
ArXiv (abs)PDFHTML

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

13 / 13 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
192
1
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
220
0
0
02 Oct 2024
Layerwise complexity-matched learning yields an improved model of
  cortical area V2
Layerwise complexity-matched learning yields an improved model of cortical area V2
Nikhil Parthasarathy
Olivier J. Hénaff
Eero P. Simoncelli
136
1
0
18 Dec 2023
CPST: Comprehension-Preserving Style Transfer for Multi-Modal Narratives
CPST: Comprehension-Preserving Style Transfer for Multi-Modal Narratives
Yi-Chun Chen
Arnav Jhala
146
0
0
14 Dec 2023
DiG-IN: Diffusion Guidance for Investigating Networks -- Uncovering
  Classifier Differences Neuron Visualisations and Visual Counterfactual
  Explanations
DiG-IN: Diffusion Guidance for Investigating Networks -- Uncovering Classifier Differences Neuron Visualisations and Visual Counterfactual ExplanationsComputer Vision and Pattern Recognition (CVPR), 2023
Maximilian Augustin
Yannic Neuhaus
Matthias Hein
DiffM
190
8
0
29 Nov 2023
ImageNet-Hard: The Hardest Images Remaining from a Study of the Power of
  Zoom and Spatial Biases in Image Classification
ImageNet-Hard: The Hardest Images Remaining from a Study of the Power of Zoom and Spatial Biases in Image ClassificationNeural Information Processing Systems (NeurIPS), 2023
Mohammad Reza Taesiri
Giang Nguyen
Sarra Habchi
Cor-Paul Bezemer
Anh Totti Nguyen
VLM
220
25
0
11 Apr 2023
Connecting metrics for shape-texture knowledge in computer vision
Connecting metrics for shape-texture knowledge in computer vision
Tiago Gaspar Oliveira
Tiago Marques
Arlindo L. Oliveira
57
0
0
25 Jan 2023
How explainable are adversarially-robust CNNs?
How explainable are adversarially-robust CNNs?
Mehdi Nourelahi
Lars Kotthoff
Peijie Chen
Anh Totti Nguyen
AAMLFAtt
152
9
0
25 May 2022
On the Properties of Adversarially-Trained CNNs
On the Properties of Adversarially-Trained CNNs
Mattia Carletti
M. Terzi
Gian Antonio Susto
AAML
96
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 visionNeural Information Processing Systems (NeurIPS), 2021
Robert Geirhos
Kantharaju Narayanappa
Benjamin Mitzkus
Tizian Thieringer
Matthias Bethge
Felix Wichmann
Wieland Brendel
VLMAAML
225
256
0
14 Jun 2021
Inverting Adversarially Robust Networks for Image Synthesis
Inverting Adversarially Robust Networks for Image SynthesisAsian Conference on Computer Vision (ACCV), 2021
Renan A. Rojas-Gomez
Raymond A. Yeh
Minh Do
A. Nguyen
124
5
0
13 Jun 2021
Do Input Gradients Highlight Discriminative Features?
Do Input Gradients Highlight Discriminative Features?Neural Information Processing Systems (NeurIPS), 2021
Harshay Shah
Prateek Jain
Praneeth Netrapalli
AAMLFAtt
185
62
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?Findings (Findings), 2020
Thang M. Pham
Trung Bui
Long Mai
Anh Totti Nguyen
449
125
0
30 Dec 2020
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