On Outlier Exposure With Generative Models

Our paper On Outlier Exposure with Generative Models has been accepted on the NeurIPS Machine Learning Safety Workshop.

Abstract

While Outlier Exposure reliably increases the performance of Out-of-Distribution detectors, it requires a set of available outliers during training. In this paper, we propose Generative Outlier Exposure (GOE), which alleviates the need for available outliers by using generative models to sample synthetic outliers from low-density regions of the data distribution. The approach requires no modification of the generator, works on image and text data, and can be used with pre-trained models. We demonstrate the effectiveness of generated outliers on several image and text datasets, including ImageNet.

Outliers generated for different datasets

Our Paper Multi-Class Hypersphere Anomaly Detection (MCHAD) has been accepted for presentation at the ICPR 2022. In summary, we propose a new loss function for learning neural networks that are able to detect anomalies in their inputs. Poster for MCHAD (PDF). How does it work? Omitting some details, the loss we propose has three different components, each of which we will explain in the following. Intra-Class Variance We want the $f(x)$ of one class to cluster as tightly around a class center $\mu_y$ as possible.

Our paper PyTorch-OOD: A library for Out-of-Distribution Detection based on PyTorch has been presented at the CVPR 2022 Workshops. You can find the most recent version of the python source code on GitHub. You can also install the package directly via pip: pip install pytorch-ood The presentation slides are available here.

On Outlier Exposure with Generative Models

Abstract

Related Posts