Summary of Benchmarking Robust Self-supervised Learning Across Diverse Downstream Tasks, by Antoni Kowalczuk et al.
Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks
by Antoni Kowalczuk, Jan Dubiński, Atiyeh Ashari Ghomi, Yi Sui, George Stein, Jiapeng Wu, Jesse C. Cresswell, Franziska Boenisch, Adam Dziedzic
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive evaluation of the robustness of self-supervised vision encoders across multiple downstream tasks, including semantic segmentation and depth estimation. The authors demonstrate that current state-of-the-art adversarial fine-tuning techniques, designed for image classification, significantly degrade clean and robust performance on other tasks. This highlights the need to enhance encoder robustness more broadly to cater to multiple applications at once. The study uses attacks operating in the encoder embedding space and at the downstream task output level, providing insights into the vulnerabilities of these models across various vision tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A big question is: how well can computers see? Large computer models have gotten really good at recognizing things like cats and cars, but what happens when we try to use them for other tasks, like understanding where objects are in a picture or how far away they are? The answer is that these models aren’t very robust – they don’t work well when someone tries to trick them. This paper looks at why this is the case and suggests ways to make these computer models more reliable. |
Keywords
» Artificial intelligence » Depth estimation » Embedding space » Encoder » Fine tuning » Image classification » Self supervised » Semantic segmentation
