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arXiv · 2101.11201

Similarity of Classification Tasks

Cuong Nguyen·Thanh-Toan Do·Gustavo Carneiro
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Abstract

Recent advances in meta-learning has led to remarkable performances on several few-shot learning benchmarks. However, such success often ignores the similarity between training and testing tasks, resulting in a potential bias evaluation. We, therefore, propose a generative approach based on a variant of Latent Dirichlet Allocation to analyse task similarity to optimise and better understand the performance of meta-learning. We demonstrate that the proposed method can provide an insightful evaluation for meta-learning algorithms on two few-shot classification benchmarks that matches common intuition: the more similar the higher performance. Based on this similarity measure, we propose a task-selection strategy for meta-learning and show that it can produce more accurate classification results than methods that randomly select training tasks.

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Cuong Nguyen, Thanh-Toan Do, Gustavo Carneiro. 2021-01-27. Similarity of Classification Tasks. https://arxiv.org/abs/2101.11201

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