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.

翻译：在小样本识别中，需要在已在一组类别上训练的分类器快速适应并泛化到不相关的新类别。为此，近期研究表明，采用精心设计的适配架构进行微调具有显著效果。然而这引出了一个问题：如何设计最优的适应策略？本文通过神经架构搜索（NAS）的视角研究此问题。给定预训练的神经网络，我们的算法能够发现适配器的最优配置，确定哪些层应保持冻结、哪些层需要微调。通过将该方法应用于残差网络和视觉Transformer，我们展示了NAS方法的通用性，并在Meta-Dataset和Meta-Album上报告了最优性能。