Web: http://arxiv.org/abs/2207.13916

Sept. 23, 2022, 1:12 a.m. | Ramya S. Hebbalaguppe, Soumya Suvra Goshal, Jatin Prakash, Harshad Khadilkar, Chetan Arora

cs.LG updates on arXiv.org arxiv.org

Modern deep neural network models are known to erroneously classify
out-of-distribution (OOD) test data into one of the in-distribution (ID)
training classes with high confidence. This can have disastrous consequences
for safety-critical applications. A popular mitigation strategy is to train a
separate classifier that can detect such OOD samples at the test time. In most
practical settings OOD examples are not known at the train time, and hence a
key question is: how to augment the ID data with synthetic …

arxiv augmentation data detection distribution

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