Summary of Variational Continual Test-time Adaptation, by Fan Lyu et al.
Variational Continual Test-Time Adaptation
by Fan Lyu, Kaile Du, Yuyang Li, Hanyu Zhao, Zhang Zhang, Guangcan Liu, Liang Wang
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 presents VCoTTA, a novel variational Bayesian approach to measure uncertainties in Continual Test-Time Adaptation (CTTA) methods that only use unlabeled test data. The prior drift can cause significant error propagation in such methods, and VCoTTA aims to mitigate this issue by introducing uncertainties into the model. Specifically, VCoTTA transforms a pre-trained deterministic model into a Bayesian Neural Network (BNN) via a variational warm-up strategy, allowing it to inject uncertainties during training. During testing, the method employs a mean-teacher update strategy using variational inference for the student model and exponential moving average for the teacher model. The paper also formulates an evidence lower bound as the cross-entropy between the student and teacher models, along with the Kullback-Leibler (KL) divergence of the prior mixture. Experimental results on three datasets demonstrate the effectiveness of VCoTTA in mitigating prior drift within the CTTA framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VCoTTA is a new way to help machines learn from new data without getting worse over time. When we use this method, called Continual Test-Time Adaptation (CTTA), it can make mistakes because the old information starts to get lost. VCoTTA fixes this problem by adding some uncertainty to the machine’s predictions. It does this by making the machine think about its own uncertainty and adjusting its answers based on that. The result is a machine that gets better at learning new things without forgetting what it already knows. |
Keywords
* Artificial intelligence * Cross entropy * Inference * Neural network * Student model * Teacher model
