  In some scenarios, classifier requires detecting out-of-distribution samples
far from its training data. With desirable characteristics, reconstruction
autoencoder-based methods deal with this problem by using input reconstruction
error as a metric of novelty vs. normality. We formulate the essence of such
approach as a quadruplet domain translation with an intrinsic bias to only
query for a proxy of conditional data uncertainty. Accordingly, an improvement
direction is formalized as maximumly compressing the autoencoder's latent space
while ensuring its reconstructive power for acting as a described domain
translator. From it, strategies are introduced including semantic
reconstruction, data certainty decomposition and normalized L2 distance to
substantially improve original methods, which together establish
state-of-the-art performance on various benchmarks, e.g., the FPR@95%TPR of
CIFAR-100 vs. TinyImagenet-crop on Wide-ResNet is 0.2%. Importantly, our method
works without any additional data, hard-to-implement structure, time-consuming
pipeline, and even harming the classification accuracy of known classes.

Computer Vision and Pattern Recognition;Machine Learning