Summary of On the Use Of Anchoring For Training Vision Models, by Vivek Narayanaswamy et al.
On the Use of Anchoring for Training Vision Models
by Vivek Narayanaswamy, Kowshik Thopalli, Rushil Anirudh, Yamen Mubarka, Wesam Sakla, Jayaraman J. Thiagarajan
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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 explores anchoring as a principle for training deep neural networks, which has been shown to improve uncertainty estimation, calibration, and extrapolation capabilities. The authors provide insights into the training and inference processes of anchored vision models and their implications for generalization and safety. However, they also identify a critical issue with anchored training that can lead to the learning of undesirable shortcuts, limiting its generalization capabilities. To address this, they introduce a new anchored training protocol that employs a simple regularizer to mitigate this issue and significantly enhances generalization. The authors empirically evaluate their proposed approach across various datasets and architectures, demonstrating substantial performance gains in generalization and safety metrics compared to the standard training protocol. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how we can make artificial intelligence (AI) more reliable and trustworthy by using a technique called anchoring. Anchoring helps AI models make better predictions and avoid learning shortcuts that don’t make sense. The authors of this paper are trying to figure out how to use anchoring for training vision models, which are like super-smart cameras that can see and understand the world. They’re also trying to fix a problem with anchored training that makes it harder for AI models to generalize, or apply what they’ve learned to new situations. The authors have come up with a new way of doing anchored training that seems to work better than the old way, and they’re testing it on different types of data and models. |
Keywords
» Artificial intelligence » Generalization » Inference
