Layer-Wise Adaptive Updating for Few-Shot Image Classification
Yunxiao Qin, Weiguo Zhang, Zezheng Wang, Chenxu Zhao, Jingping Shi

TL;DR
This paper introduces a layer-wise adaptive updating method for meta-learning in few-shot image classification, improving learning efficiency and outperforming existing methods by focusing on updating top layers more effectively.
Contribution
The paper proposes a novel meta-learning approach that learns a layer-wise adaptive updating rule, emphasizing top layer updates for better few-shot learning performance.
Findings
Outperforms existing few-shot classification methods.
Learns from few images at least 5 times more efficiently.
Achieves significant margin improvements in classification accuracy.
Abstract
Few-shot image classification (FSIC), which requires a model to recognize new categories via learning from few images of these categories, has attracted lots of attention. Recently, meta-learning based methods have been shown as a promising direction for FSIC. Commonly, they train a meta-learner (meta-learning model) to learn easy fine-tuning weight, and when solving an FSIC task, the meta-learner efficiently fine-tunes itself to a task-specific model by updating itself on few images of the task. In this paper, we propose a novel meta-learning based layer-wise adaptive updating (LWAU) method for FSIC. LWAU is inspired by an interesting finding that compared with common deep models, the meta-learner pays much more attention to update its top layer when learning from few images. According to this finding, we assume that the meta-learner may greatly prefer updating its top layer to…
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