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: 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 com