Neural Fine-Tuning Search for Few-Shot Learning

• URL: http://arxiv.org/abs/2306.09295v1
• Date: Thu, 15 Jun 2023 17:20:35 GMT
• Title: Neural Fine-Tuning Search for Few-Shot Learning
• Authors: Panagiotis Eustratiadis, {\L}ukasz Dudziak, Da Li, Timothy Hospedales
• Abstract summary: In few-shot recognition, a classifier is required to rapidly adapt and generalize to a disjoint, novel set of classes. Recent studies have shown the efficacy of fine-tuning with carefully crafted adaptation architectures. We study this question through the lens of neural architecture search (NAS)
• Score: 10.194808064624771
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In few-shot recognition, a classifier that has been trained on one set of classes is required to rapidly adapt and generalize to a disjoint, novel set of classes. To that end, recent studies have shown the efficacy of fine-tuning with carefully crafted adaptation architectures. However this raises the question of: How can one design the optimal adaptation strategy? In this paper, we study this question through the lens of neural architecture search (NAS). Given a pre-trained neural network, our algorithm discovers the optimal arrangement of adapters, which layers to keep frozen and which to fine-tune. We demonstrate the generality of our NAS method by applying it to both residual networks and vision transformers and report state-of-the-art performance on Meta-Dataset and Meta-Album.

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