Author Interview - VOS: Learning What You Don't Know by Virtual Outlier Synthesis

#deeplearning #objectdetection #outliers

An interview with the authors of "Virtual Outlier Synthesis".

Watch the paper review video here: https://youtu.be/i-J4T3uLC9M

Outliers are data points that are highly unlikely to be seen in the training distribution, and therefore deep neural networks have troubles when dealing with them. Many approaches to detecting outliers at inference time have been proposed, but most of them show limited success. This paper presents Virtual Outlier Synthesis, which is a method that pairs synthetic outliers, forged in the latent space, with an energy-based regularization of the network at training time. The result is a deep network that can reliably detect outlier datapoints during inference with minimal overhead.

OUTLINE:

0:00 - Intro

2:20 - What was the motivation behind this paper?

5:30 - Why object detection?

11:05 - What's the connection to energy-based models?

12:15 - Is a Gaussian mixture model appropriate for high-dimensional data?

16:15 - What are the most important components of the method?

18:30 - What are the downstream effects of the regularizer?

22:00 - Are there severe trade-offs to outlier detection?

23:55 - Main experimental takeaways?

26:10 - Why do outlier detection in the last layer?

30:20 - What does it take to finish a research projects successfully?

Paper: https://arxiv.org/abs/2202.01197

Code: https://github.com/deeplearning-wisc/vos

Abstract:

Out-of-distribution (OOD) detection has received much attention lately due to its importance in the safe deployment of neural networks. One of the key challenges is that models lack supervision signals from unknown data, and as a result, can produce overconfident predictions on OOD data. Previous approaches rely on real outlier datasets for model regularization, which can be costly and sometimes infeasible to obtain in practice. In this paper, we present VOS, a novel framework for OOD detection by adaptively synthesizing virtual outliers that can meaningfully regularize the model's decision boundary during training. Specifically, VOS samples virtual outliers from the low-likelihood region of the class-conditional distribution estimated in the feature space. Alongside, we introduce a novel unknown-aware training objective, which contrastively shapes the uncertainty space between the ID data and synthesized outlier data. VOS achieves state-of-the-art performance on both object detection and image classification models, reducing the FPR95 by up to 7.87% compared to the previous best method. Code is available at this https URL.

Authors: Xuefeng Du, Zhaoning Wang, Mu Cai, Yixuan Li

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