Author:
Taylor-Melanson Will,Sadeghi Zahra,Matwin Stan
Funder
Natural Sciences and Engineering Research Council of Canada
Publisher
Springer Science and Business Media LLC
Reference29 articles.
1. de Castro DC, Tan J, Kainz B, et al (2018) Morpho-mnist: Quantitative assessment and diagnostics for representation learning. CoRR arXiv:1809.10780
2. Dash S, Balasubramanian VN, Sharma A (2022) Evaluating and mitigating bias in image classifiers: a causal perspective using counterfactuals. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp 915–924
3. Deng L (2012) The mnist database of handwritten digit images for machine learning research. IEEE Signal Process Mag 29(6):141–142
4. Dhurandhar A, Chen PY, Luss R et al (2018) Explanations based on the missing: towards contrastive explanations with pertinent negatives. arXiv:1802.07623
5. Dinh L, Krueger D, Bengio Y (2014) Nice: non-linear independent components estimation. arXiv preprint arXiv:1410.8516