DiffKendall: A Novel Approach for Few-Shot Learning with Differentiable
Kendall's Rank Correlation
- URL: http://arxiv.org/abs/2307.15317v2
- Date: Tue, 24 Oct 2023 06:53:55 GMT
- Title: DiffKendall: A Novel Approach for Few-Shot Learning with Differentiable
Kendall's Rank Correlation
- Authors: Kaipeng Zheng, Huishuai Zhang, Weiran Huang
- Abstract summary: Few-shot learning aims to adapt models trained on the base dataset to novel tasks where the categories were not seen by the model before.
This often leads to a relatively uniform distribution of feature values across channels on novel classes.
We show that the importance ranking of feature channels is a more reliable indicator for few-shot learning than geometric similarity metrics.
- Score: 16.038667928358763
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Few-shot learning aims to adapt models trained on the base dataset to novel
tasks where the categories were not seen by the model before. This often leads
to a relatively uniform distribution of feature values across channels on novel
classes, posing challenges in determining channel importance for novel tasks.
Standard few-shot learning methods employ geometric similarity metrics such as
cosine similarity and negative Euclidean distance to gauge the semantic
relatedness between two features. However, features with high geometric
similarities may carry distinct semantics, especially in the context of
few-shot learning. In this paper, we demonstrate that the importance ranking of
feature channels is a more reliable indicator for few-shot learning than
geometric similarity metrics. We observe that replacing the geometric
similarity metric with Kendall's rank correlation only during inference is able
to improve the performance of few-shot learning across a wide range of methods
and datasets with different domains. Furthermore, we propose a carefully
designed differentiable loss for meta-training to address the
non-differentiability issue of Kendall's rank correlation. By replacing
geometric similarity with differentiable Kendall's rank correlation, our method
can integrate with numerous existing few-shot approaches and is ready for
integrating with future state-of-the-art methods that rely on geometric
similarity metrics. Extensive experiments validate the efficacy of the
rank-correlation-based approach, showcasing a significant improvement in
few-shot learning.
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