Summary of Asynchronous Perception Machine For Efficient Test-time-training, by Rajat Modi et al.
Asynchronous Perception Machine For Efficient Test-Time-Training
by Rajat Modi, Yogesh Singh Rawat
First submitted to arxiv on: 27 Oct 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 proposed Asynchronous Perception Machine (APM) is a computationally-efficient architecture for test-time-training (TTT). APM can process patches of an image one at a time in any order, still encoding semantic-awareness in the network. It demonstrates ability to recognize out-of-distribution images without dataset-specific pre-training, augmentation or any-pretext task, offering competitive performance over existing TTT approaches. APM distills test sample’s representation once and learns using just this single representation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary APM is a new way for computers to learn from pictures. It can look at an image in pieces, one by one, and still understand what’s going on. This means it doesn’t need special training or extra information to recognize things that are different from what it was trained on. APM is good at doing this job compared to other methods. |
