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Fantastic DNN Classifiers and How to Identify them without Data

Fantastic DNN Classifiers and How to Identify them without Data

24 May 2023
Nathaniel R. Dean
D. Sarkar
ArXiv (abs)PDFHTML

Papers citing "Fantastic DNN Classifiers and How to Identify them without Data"

15 / 15 papers shown
Title
Reconstructing Training Data from Trained Neural Networks
Reconstructing Training Data from Trained Neural Networks
Niv Haim
Gal Vardi
Gilad Yehudai
Ohad Shamir
Michal Irani
89
141
0
15 Jun 2022
Supervised Contrastive Replay: Revisiting the Nearest Class Mean
  Classifier in Online Class-Incremental Continual Learning
Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning
Zheda Mai
Ruiwen Li
Hyunwoo J. Kim
Scott Sanner
CLL
78
186
0
22 Mar 2021
NeurIPS 2020 Competition: Predicting Generalization in Deep Learning
NeurIPS 2020 Competition: Predicting Generalization in Deep Learning
Yiding Jiang
Pierre Foret
Scott Yak
Daniel M. Roy
H. Mobahi
Gintare Karolina Dziugaite
Samy Bengio
Suriya Gunasekar
Isabelle M Guyon
Behnam Neyshabur Google Research
OOD
64
55
0
14 Dec 2020
Representation Based Complexity Measures for Predicting Generalization
  in Deep Learning
Representation Based Complexity Measures for Predicting Generalization in Deep Learning
Parth Natekar
Manik Sharma
50
36
0
04 Dec 2020
Understanding the Failure Modes of Out-of-Distribution Generalization
Understanding the Failure Modes of Out-of-Distribution Generalization
Vaishnavh Nagarajan
Anders Andreassen
Behnam Neyshabur
OODOODD
61
177
0
29 Oct 2020
Adversarial Defense by Restricting the Hidden Space of Deep Neural
  Networks
Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks
Aamir Mustafa
Salman Khan
Munawar Hayat
Roland Göcke
Jianbing Shen
Ling Shao
AAML
62
152
0
01 Apr 2019
Model Evaluation, Model Selection, and Algorithm Selection in Machine
  Learning
Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning
S. Raschka
124
784
0
13 Nov 2018
Adversarial Attacks and Defences: A Survey
Adversarial Attacks and Defences: A Survey
Anirban Chakraborty
Manaar Alam
Vishal Dey
Anupam Chattopadhyay
Debdeep Mukhopadhyay
AAMLOOD
86
683
0
28 Sep 2018
This Looks Like That: Deep Learning for Interpretable Image Recognition
This Looks Like That: Deep Learning for Interpretable Image Recognition
Chaofan Chen
Oscar Li
Chaofan Tao
A. Barnett
Jonathan Su
Cynthia Rudin
250
1,188
0
27 Jun 2018
RepMet: Representative-based metric learning for classification and
  one-shot object detection
RepMet: Representative-based metric learning for classification and one-shot object detection
Leonid Karlinsky
J. Shtok
Sivan Harary
Eli Schwartz
Mattias Marder
Rogerio Feris
Raja Giryes
A. Bronstein
VLMObjD
121
319
0
12 Jun 2018
Robustness May Be at Odds with Accuracy
Robustness May Be at Odds with Accuracy
Dimitris Tsipras
Shibani Santurkar
Logan Engstrom
Alexander Turner
Aleksander Madry
AAML
108
1,782
0
30 May 2018
Adversarially Robust Generalization Requires More Data
Adversarially Robust Generalization Requires More Data
Ludwig Schmidt
Shibani Santurkar
Dimitris Tsipras
Kunal Talwar
Aleksander Madry
OODAAML
155
795
0
30 Apr 2018
Deep Learning for Case-Based Reasoning through Prototypes: A Neural
  Network that Explains Its Predictions
Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions
Oscar Li
Hao Liu
Chaofan Chen
Cynthia Rudin
178
592
0
13 Oct 2017
Towards Deep Learning Models Resistant to Adversarial Attacks
Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry
Aleksandar Makelov
Ludwig Schmidt
Dimitris Tsipras
Adrian Vladu
SILMOOD
317
12,131
0
19 Jun 2017
DeepFool: a simple and accurate method to fool deep neural networks
DeepFool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli
Alhussein Fawzi
P. Frossard
AAML
154
4,905
0
14 Nov 2015
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