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On Out-of-distribution Detection with Energy-based Models
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Several density estimation methods have shown to fail to detect out-of-distribution (OOD) samples by assigning higher likelihoods to anomalous data. Energy-based models (EBMs) are flexible, unnormalized density models which seem to be able to improve upon this failure mode. In this work, we provide an extensive study investigating OOD detection with EBMs trained with different approaches on tabular and image data and find that EBMs do not provide consistent advantages. We hypothesize that EBMs do not learn semantic features despite their discriminative structure similar to Normalizing Flows. To verify this hypotheses, we show that supervision and architectural restrictions improve the OOD detection of EBMs independent of the training approach.
Forward citations
Cited by 2 Pith papers
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ALSA: Anchors in Logit Space for Out-of-Distribution Accuracy Estimation
ALSA learns anchors in logit space and uses their influence on unlabeled samples to estimate model accuracy under distribution shift.
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A Variational Information Theoretic Approach to Out-of-Distribution Detection
A variational loss combining KL divergence and the Information Bottleneck predicts a piecewise-linear shaping function for OOD detection that beats existing element-wise shaping methods on ImageNet and CIFAR benchmarks.
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