  Out-of-distribution (OOD) detection approaches usually present special
requirements (e.g., hyperparameter validation, collection of outlier data) and
produce side effects (e.g., classification accuracy drop, slower
energy-inefficient inferences). We argue that these issues are a consequence of
the SoftMax loss anisotropy and disagreement with the maximum entropy
principle. Thus, we propose the IsoMax loss and the entropic score. The
seamless drop-in replacement of the SoftMax loss by IsoMax loss requires
neither additional data collection nor hyperparameter validation. The trained
models do not exhibit classification accuracy drop and produce fast
energy-efficient inferences. Moreover, our experiments show that training
neural networks with IsoMax loss significantly improves their OOD detection
performance. The IsoMax loss exhibits state-of-the-art performance under the
mentioned conditions (fast energy-efficient inference, no classification
accuracy drop, no collection of outlier data, and no hyperparameter
validation), which we call the seamless OOD detection task. In future work,
current OOD detection methods may replace the SoftMax loss with the IsoMax loss
to improve their performance on the commonly studied non-seamless OOD detection
problem.
