Neural Networks Out-of-Distribution Detection: Hyperparameter-Free Isotropic Maximization Loss, The Principle of Maximum Entropy, Cold Training, and Branched Inferences

Current out-of-distribution detection (ODD) approaches present severe drawbacks that make impracticable their large scale adoption in real-world applications. In this paper, we propose a novel loss called Hyperparameter-Free IsoMax that overcomes these limitations... (read more)

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