Kimin Lee, Honglak Lee, Kibok Lee, Jinwoo Shin. Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples. ICLR 2018.

Lee et al. propose a generative model for obtaining confidence-calibrated classifiers. Neural networks are known to be overconfident in their predictions – not only on examples from the task’s data distribution, but also on other examples taken from different distributions. The authors propose a GAN-based approach to force the classifier to predict uniform predictions on examples not taken from the data distribution. In particular, in addition to the target classifier, a generator and a discriminator are introduced. The generator generates “hard” out-of-distribution examples; ideally these examples are close to the in-distribution, i.e., the data distribution of the actual task. The discriminator is intended to distinguish between out- and in-distribution. The overall algorithm, including the necessary losses, is given in Algorithm 1. In experiments, the approach is shown to allow detecting out-distribution examples nearly perfectly. Examples of the generated “hard” out-of-distribution samples are given in Figure 1.

Algorithm 1: The proposed joint training scheme of out-distribution generator $G$, the in-/out-distribution discriminator $G$ and the original classifier providing $P_\theta$(y|x)$ with parameters $\theta$.

Figure 1: A comparison of a regular GAN (a and c) to the proposed framework (c and d). Clearly, the proposed approach generates out-of-distribution samples (i.e., no meaningful digits) close to the original data distribution.

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