LG AI Research· EXAONE

[NeurIPS 2023] Out-of-Distribution Detection via Synthetic Outlier Generation Part 2

In the first part, we introduced the LG AI Research’s Data Intelligence (DI) Lab study, “Diffusion-Based Semantic-Discrepant Outlier Generation for Out-of-Distribution Detection” which was presented at the NeurIPS 2023 SyntheticData4ML Workshop. In this second part, we would like to share other notable papers on outlier generation that are worth reading. As OOD (Out-of-Distribution) data cannot actually be seen, it is very important to properly define OOD for outlier generation. The papers covered in this part show how to define OOD in the text domain and image latent domain, respectively. Let’s take a closer look at each paper. ▶ Out-of-Distribution Detection via Synthetic Outlier Generation ㆍ Part 1 DI Lab Suhee Yoon ( Link ) ㆍ Part 2 DI Lab Sanghyu Yoon 1. On the Powerfulness of Textual Outlier Exposure for Visual OOD Detection [1] This study is notable for being the first to try textual outlier exposure to improve the performance of visual OOD detection. Existing outlier exposure relied on visual outliers in the image domain due to the nature of neural networks that handle single-modal data. …

Announced

13 Feb 2024 00:00 UTC

Detected

28 Aug 2026 22:20 UTC (+927d after)