Published as a conference paper at ICLR 2018 TRAINING CONFIDENCE-CALIBRATED CLASSIFIERS FOR DETECTING OUT-OF-DISTRIBUTION SAMPLES
The problem of detecting whether a test sample is from in-distribution (i.e., training distribution by a classifier) or out-of-distribution sufficiently different from it arises in many real-world machine learning applications. However, the state-of-art deep neural networks are known to be highly overconfident in their predictions, i.e., do not distinguish in-and out-of-distributions. Recently, to handle this issue, several threshold-based detectors have been proposed given pre-trained neural classifiers. However, the performance of prior works highly depends on how to train the classifiers since they only focus on improving inference procedures. In this paper, we develop a novel training method for classifiers so that such inference algorithms can work better. In particular, we suggest two additional terms added to the original loss (e.g., cross entropy). The first one forces samples from out-of-distribution less confident by the classifier and the second one is for (implicitly) generating most effective training samples for the first one. In essence, our method jointly trains both classification and generative neural networks for out-of-distribution. We demonstrate its effectiveness using deep convolutional neural networks on various popular image datasets.
This paper proposes a new method of detecting in vs. out of distribution samples. Most existing approaches for this deal with detecting out of distributions at *test time* by augmenting input data and or temperature scaling the softmax and applying a simple classification rule based on the output. This paper proposes a different approach (with could be combined with these methods) based on a new training procedure. 
The authors propose to train a generator network in combination with the classifier and an adversarial discriminator. The generator is trained to produce images that (1) fools a standard GAN discriminator and (2) has high entropy (as enforced with the pull-away term from the EBGAN). Classifier is trained to not only maximize classification accuracy on the real training data but also to output a uniform distribution for the generated samples. 
The model is evaluated on CIFAR-10 and SVNH, where several out of distribution datasets are used in each case. Performance gains are clear with respect to the baseline methods.
This paper is clearly written, proposes a simple model and seems to outperform current methods. One thing missing is a discussion of how this approach is related to semi-supervised learning approaches using GANS where a generative model produces extra data points for the classifier/discriminator. 
 I have some clarifying questions below:
- Figure 4 is unclear: does "Confidence loss with original GAN" refer to the method where the classifier is pretrained and then "Joint confidence loss" is with joint training? What does "Confidence loss (KL on SVHN/CIFAR-10)" refer to?
- Why does the join training improve the ability of the model to generalize to out-of-distribution datasets not seen during training?
- Why is the pull away term necessary and how does the model perform without it? Most GAN models are able to stably train without such explicit terms such as the pull away or batch discrimination. Is the proposed model unstable without the pull-away term? 
- How does this compare with a method whereby instead of pushing the fake sample's softmax distribution to be uniform, the model is simply a trained to classify them as an additional "out of distribution" class? This exact approach has been used to do semi supervised learning with GANS [1][2]. More generally, could the authors comment on how this approach is related to these semi-supervised approaches? 
- Did you try combining the classifier and discriminator into one model as in [1][2]?
[1] Semi-Supervised Learning with Generative Adversarial Networks (https://arxiv.org/abs/1606.01583)
[2] Good Semi-supervised Learning that Requires a Bad GAN (https://arxiv.org/abs/1705.09783)