text: This paper presents a novel approach to calibrate classifiers for out of distribution samples. In additional to the original cross entropy loss, the “confidence loss”  was proposed to guarantee the out of distribution points have low confidence in the classifier. As out of distribution samples are hard to obtain, authors also propose to use GAN generating “boundary” samples as out of distribution samples. The problem setting is new and objective (1) is interesting and reasonable. However, I am not very convinced that objective (3) will generate boundary samples. Suppose that theta is set appropriately so that p_theta (y|x) gives a uniform distribution over labels for out of distribution samples. Because of the construction of U(y), which uniformly assign labels to generated out of distribution samples, the conditional probability p_g (y|x) should always be uniform so p_g (y|x) divided by p_theta (y|x) is almost always 1. The KL divergence in (a) of (3) should always be approximately 0 no matter what samples are generated. I also have a few other concerns: 1. There seems to be a related work: [1] Perello-Nieto et al., Background Check: A general technique to build more reliable and versatile classifiers, ICDM 2016, Where authors constructed a classifier, which output K+1 labels and the K+1-th label is the “background noise” label for this classification problem. Is the method in [1] applicable to this paper’s setting? Moreover, [1] did not seem to generate any out of distribution samples. 2. I am not so sure that how the actual out of distribution detection was done (did I miss something here?). Authors repeatedly mentioned “maximum prediction values”, but it was not defined throughout the paper. Algorithm 1. is called “minimization for detection and generating out of distribution (samples)”, but this is only gradient descent, right? I do not see a detection procedure. Given the title also contains “detecting”, I feel authors should write explicitly how the detection is done in the main body.