Defuse: Harnessing Unrestricted Adversarial Examples for Debugging Models Beyond Test Accuracy
@article{Slack2021DefuseHU,
title={Defuse: Harnessing Unrestricted Adversarial Examples for Debugging Models Beyond Test Accuracy},
author={Dylan Slack and N. Rauschmayr and K. Kenthapadi},
journal={ArXiv},
year={2021},
volume={abs/2102.06162}
}We typically compute aggregate statistics on held-out test data to assess the generalization of machine learning models. However, statistics on test data often overstate model generalization, and thus, the performance of deployed machine learning models can be variable and untrustworthy. Motivated by these concerns, we develop methods to automatically discover and correct model errors beyond those available in the data. We propose Defuse, a method that generates novel model misclassifications… Expand
Figures from this paper
figure 1 
figure 2 
figure 3 
figure 4 
figure 5 
figure 6 
figure 7 
figure 8 
figure 9 
figure 10 
figure 11 
figure 12 
figure 13 
figure 14 
figure 16 
figure 17 
figure 18 
figure 19 
figure 20 
figure 21 
figure 22 
figure 23 
figure 24 
figure 25 
figure 26 
figure 27 
figure 28 
figure 29 
figure 30 
figure 31 
figure 32 
figure 33 
figure 34 
figure 35 
figure 36 
figure 37 
figure 38 
figure 39 
figure 40 
figure 41
References
SHOWING 1-10 OF 39 REFERENCES
Generating Natural Adversarial Examples
- Computer Science, Mathematics
- ICLR
- 2018
- 275
- Highly Influential
- PDF
TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing
- Computer Science, Mathematics
- ICML
- 2019
- 123
- PDF
Constructing Unrestricted Adversarial Examples with Generative Models
- Computer Science, Mathematics
- NeurIPS
- 2018
- 120
- Highly Influential
- PDF
Socratic Learning: Augmenting Generative Models to Incorporate Latent Subsets in Training Data
- Computer Science, Mathematics
- 2016
- 21
- PDF