A Study on Out-of-Distribution Detection based on Generative Models Trained for Each Discriminant Class

Author:

MATSUNAE Ryota1,SAITO Fuyu1,YAMASHITA Haruka2,GOTO Masayuki1

Affiliation:

1. Waseda University

2. Sophia University

Publisher

The Japanese Society for Quality Control

Subject

Anesthesiology and Pain Medicine

Reference21 articles.

1. Choi, H., Jang, E., and Alemi, A. A. (2018): “Waic, but why? generative ensembles for robust anomaly detection”, arXiv preprint arXiv:1810.01392.

2. Chow, C. K. (1970): “On optimum recognition error and reject tradeoff”, IEEE Transactions on Information Theory, 16(1), pp 41-46.

3. Dinh, L., Sohl-Dickstein, J., and Bengio, S. (2016): “Density estimation using real nvp”, arXiv preprint arXiv:1605.08803.

4. Foggia, P., Sansone, C., Tortorella, F., and Vento, M. (1999): “Multiclassification: Reject criteria for the Bayesian combiner”, Pattern Recognition, 32(8), pp 1435-1447.

5. Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014): “Generative adversarial networks”, arXiv preprint arXiv:1406.2661.

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