VOS: Learning What You Don't Know by Virtual Outlier Synthesis (Paper Explained)

VOS: Learning What You Don't Know by Virtual Outlier Synthesis (Paper Explained)

Mar 14, 2022
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#vos #outliers #deeplearning Sponsor: Assembly AI Check them out here: https://www.assemblyai.com/?utm_source=youtube&utm_medium=social&utm_campaign=yannic1 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 2:00 - Sponsor: Assembly AI (Link below) 4:05 - Paper Overview 6:45 - Where do traditional classifiers fail? 11:00 - How object detectors work 17:00 - What are virtual outliers and how are they created? 24:00 - Is this really an appropriate model for outliers? 26:30 - How virtual outliers are used during training 34:00 - Plugging it all together to detect outliers 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 Links: Merch: http://store.ykilcher.com TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yannic-kilcher LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/2017636191 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

0:00 - Intro 2:00 - Sponsor: Assembly AI (Link below) 4:05 - Paper Overview 6:45 - Where do traditional classifiers fail? 11:00 - How object detectors work 17:00 - What are virtual outliers and how are they created? 24:00 - Is this really an appropriate model for outliers? 26:30 - How virtual outliers are used during training 34:00 - Plugging it all together to detect outliers
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