Summary of Adashadow: Responsive Test-time Model Adaptation in Non-stationary Mobile Environments, by Cheng Fang et al.
AdaShadow: Responsive Test-time Model Adaptation in Non-stationary Mobile Environments
by Cheng Fang, Sicong Liu, Zimu Zhou, Bin Guo, Jiaqi Tang, Ke Ma, Zhiwen Yu
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 The paper proposes AdaShadow, a test-time adaptation framework for non-stationary mobile data distribution and resource dynamics. It aims to reduce the latency in on-device adapting to continual domain shifts in applications like autonomous driving and augmented reality. The proposed method selectively updates adaptation-critical layers using a backpropagation-free assessor, unit-based runtime predictor, and online scheduler. Additionally, it incorporates a memory I/O-aware computation reuse scheme to further reduce latency. Experimental results show that AdaShadow achieves the best accuracy-latency balance under continual shifts with 2x to 3.5x speedup over state-of-the-art TTA methods and 14.8% to 25.4% accuracy boost over efficient supervised methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine using your smartphone or car’s computer in a new, changing environment without any hiccups. This paper helps with that by creating a way for devices to quickly adjust to new situations. It’s called “test-time adaptation” and it’s important for things like self-driving cars and augmented reality games. The authors propose a new method called AdaShadow that can adapt quickly while keeping the device’s processing power low. They tested it and found that it works well, even better than other methods that are similar. This technology could make our devices more useful and easier to use in different situations. |
Keywords
» Artificial intelligence » Backpropagation » Supervised
