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Title: Deep Generative Classifier for Out-of-distribution Sample Detection

Paper content:
The capability of reliably detecting out-of-distribution samples is one of the key factors in deploying a good classifier, as the test distribution always does not match with the training distribution in most real-world applications.
In this work, we propose a deep generative classifier which is effective to detect out-of-distribution samples as well as classify in-distribution samples, by integrating the concept of Gaussian discriminant analysis into deep neural networks.
Unlike the discriminative (or softmax) classifier that only focuses on the decision boundary partitioning its latent space into multiple regions, our generative classifier aims to explicitly model class-conditional distributions as separable Gaussian distributions.
Thereby, we can define the confidence score by the distance between a test sample and the center of each distribution.
Our empirical evaluation on multi-class images and tabular data demonstrate that the generative classifier achieves the best performances in distinguishing out-of-distribution samples, and also it can be generalized well for various types of deep neural networks.
Out-of-distribution (OOD) detection, also known as novelty detection, refers to the task of identifying the samples that differ in some respect from the training samples.
Recently, deep neural networks (DNNs) turned out to show unpredictable behaviors in case of mismatch between the training and testing data distributions; for example, they tend to make high confidence prediction for the samples that are drawn from OOD or belong to unseen classes (Szegedy et al., 2014; Moosavi-Dezfooli et al., 2017) .
For this reason, accurately measuring the distributional uncertainty (Malinin & Gales, 2018) of DNNs becomes one of the important challenges in many real-world applications where we can hardly control the testing data distribution.
Several recent studies have tried to simply detect OOD samples using the confidence score defined by softmax probability (Hendrycks & Gimpel, 2017; Liang et al., 2018) or Mahalanobis distance from class means (Lee et al., 2018) , and they showed promising results even without re-training the model.
However, all of them employ the DNNs designed for a discriminative (or softmax) classifier, which has limited power to locate OOD samples distinguishable with in-distribution (ID) samples in their latent space.
To be specific, the softmax classifier is optimized to learn the discriminative latent space where the training samples are aligned along their corresponding class weight vectors, maximizing the softmax probability for the target classes.
As pointed out in (Hendrycks & Gimpel, 2017) , OOD samples are more likely to have small values of the softmax probability for all known classes, which means that their latent vectors get closer to the origin.
As a result, there could be a large overlap between two sets of ID and OOD samples in the latent space (Figure 1 ), which eventually reduces the gap between their confidence scores and degrades the performance as well.
In addition, most of existing confidence scores adopt additional calibration techniques Hinton et al., 2015) to enhance the reliability of the detection, but they include several hyperparameters whose optimal values vary depending on the testing data distribution.
In this situation, they utilized a small portion of each test set (containing both ID and OOD samples) for validation, and reported the results evaluated on the rest by using the optimal hyperparameter values for each test case.
Considering the motivation of OOD detection that prior knowledge of test distributions is not available before we encounter them, such process of tuning the hyperparameters for each test case is not practical when deploying the DNNs in practice.
In this paper, we propose a novel objective to train DNNs with a generative (or distance) classifier which is capable of effectively identifying OOD test samples.
The main difference of our deep generative classifier is to learn separable class-conditional distributions in the latent space, by explicitly modeling them as a DNN layer.
The generative classifier places OOD samples further apart from the distributions of all given classes, without utilizing OOD samples for its validation.
Thus, based on the Euclidean distance between a test sample and the centers of the obtained class-conditional distributions, we can calculate how likely and how confidently the sample belongs to each class.
This can be interpreted as a multi-class extension of unsupervised anomaly detection (Ruff et al., 2018) , and Gaussian discriminant analysis provides the theoretical background for incorporating the generative classifier into the DNNs.
Our extensive experiments on images and tabular data demonstrate that the proposed classifier distinguishes OOD samples more accurately than the state-of-the-art method, while maintaining the classification accuracy for ID samples.
This paper introduces a deep learning objective to learn the multi-class generative classifier, by fusing the concept of Gaussian discriminant analysis with DNNs.
Unlike the conventional softmax classifier, our generative (or distance) classifier learns the class-conditional distributions to be separated from each other and follow the Gaussian distribution at the same time, thus it is able to effectively distinguish OOD samples from ID samples.
We empirically show that our confidence score beats other competing methods in detecting both OOD tabular data and OOD images, and also the distance classifier can be easily combined with various types of DNNs to further improve their performances.

assistant: text: This paper proposes a deep generative classifier which is effective to detect out-of-distribution samples as well as classify in-distribution samples, by integrating the concept of Gaussian discriminant analysis into deep neural networks.