Building robust classifiers through generation of confident out of distribution examples

Abstract

Deep learning models are known to be overconfident in their predictions on out of distribution inputs. There have been several pieces of work to address this issue, including a number of approaches for building Bayesian neural networks, as well as closely related work on detection of out of distribution samples. Recently, there has been work on building classifiers that are robust to out of distribution samples by adding a regularization term that maximizes the entropy of the classifier output on out of distribution data. To approximate out of distribution samples (which are not known apriori), a GAN was used for generation of samples at the edges of the training distribution. In this paper, we introduce an alternative GAN based approach for building a robust classifier, where the idea is to use the GAN to explicitly generate out of distribution samples that the classifier is confident on (low entropy), and have the classifier maximize the entropy for these samples. We showcase the effectiveness of our approach relative to state-of-the-art on hand-written characters as well as on a variety of natural image datasets.

arXiv commentThird workshop on Bayesian Deep Learning (NeurIPS 2018), Montréal, Canada

References in corpus (7)

• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
• LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
• A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
• EMNIST: an extension of MNIST to handwritten letters
• Dropout Inference in Bayesian Neural Networks with Alpha-divergences

Cited by in corpus (7)

• Out-of-distribution Detection in Classifiers via Generation
• Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection
• Analysis of Confident-Classifiers for Out-of-distribution Detection
• BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty
• On Out-of-distribution Detection with Energy-based Models
• OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary
• FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection