Summary of Learning to Generate Gradients For Test-time Adaptation Via Test-time Training Layers, by Qi Deng et al.
Learning to Generate Gradients for Test-Time Adaptation via Test-Time Training Layers
by Qi Deng, Shuaicheng Niu, Ronghao Zhang, Yaofo Chen, Runhao Zeng, Jian Chen, Xiping Hu
First submitted to arxiv on: 22 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 proposed Meta Gradient Generator (MGG) tackles test-time adaptation (TTA) by learning an optimizer that utilizes historical gradient information to fine-tune trained models online. This approach, unlike traditional TTA using manually designed optimizers like SGD, addresses issues with noisy learning signals and unstable optimization processes. MGG incorporates a lightweight sequence modeling layer called the gradient memory layer, which compresses historical gradients into network parameters for effective memorization during long-term adaptation. The method requires only a small number of unlabeled samples for pre-training and can be deployed to process unseen data. Compared to state-of-the-art methods like SAR, MGG achieves 7.4% accuracy improvement and 4.2 times faster adaptation speed on ImageNet-C. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Test-time adaptation (TTA) helps models adjust to new environments or out-of-distribution data without labeled training data. However, this process can be noisy and unstable, making it hard for models to converge quickly. The Meta Gradient Generator (MGG) solves this problem by learning an optimizer that uses past gradient information to fine-tune models online. MGG is better than previous methods because it requires less data and adapts faster. |
Keywords
» Artificial intelligence » Optimization
