Summary of Beyond Perceptual Distances: Rethinking Disparity Assessment For Out-of-distribution Detection with Diffusion Models, by Kun Fang et al.
Beyond Perceptual Distances: Rethinking Disparity Assessment for Out-of-Distribution Detection with Diffusion Models
by Kun Fang, Qinghua Tao, Zuopeng Yang, Xiaolin Huang, Jie Yang
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores Out-of-Distribution (OoD) detection, a crucial problem in machine learning that aims to determine whether a sample is from the same distribution as the training data or not. Recently, Diffusion Models (DMs) have been used to detect OoD by calculating the perceptual distance between an input image and its generated counterpart using DMs. The study demonstrates how DM-based methods can shed new light on this topic, although they remain relatively unexplored. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a way for computers to figure out if something they’re looking at is part of what they were trained on or not. It’s called Out-of-Distribution detection. One new approach uses special computer models called Diffusion Models (DMs) that can create fake versions of images. By comparing the real image with its fake version, DMs can help computers detect when something is outside their normal training data. |
Keywords
» Artificial intelligence » Diffusion » Machine learning
