In cross-domain few-shot classification (CFC), recent works mainly focus on adapting a simple transformation head on top of a frozen pre-trained backbone with few labeled data to project embeddings into a task-specific metric space where classification can be performed by measuring similarities between image instance and prototype representations. Technically, an assumption implicitly adopted in such a framework is that the prototype and image instance embeddings share the same representation transformation. However, in this paper, we find that there naturally exists a gap, which resembles the modality gap, between the prototype and image instance embeddings extracted from the frozen pre-trained backbone, and simply applying the same transformation during the adaptation phase constrains exploring the optimal representations and shrinks the gap between prototype and image representations. To solve this problem, we propose a simple yet effective method, contrastive prototype-image adaptation (CoPA), to adapt different transformations respectively for prototypes and images similarly to CLIP by treating prototypes as text prompts. Extensive experiments on Meta-Dataset demonstrate that CoPA achieves the state-of-the-art performance more efficiently. Meanwhile, further analyses also indicate that CoPA can learn better representation clusters, enlarge the gap, and achieve minimal validation loss at the enlarged gap.

In cross-domain few-shot classification, nearest Cross-domain few-shot classification (Dvornik et al., 2020; centroid classifier (NCC) aims to learn representations Li et al., 2021a; Liu et al., 2021a; Triantafillou et al., 2020), to construct a metric space where few-shot also known as CFC, is a learning paradigm which aims at classification can be performed by measuring the learning to perform classification on tasks sampled from similarities between samples and the prototype of previously unseen data or domains with only a few labeled each class. An intuition behind NCC is that each data available. Compared with conventional few-shot classification sample is pulled closer to the class centroid it belongs (Finn et al., 2017; Ravi & Larochelle, 2017; Snell to while pushed away from those of other et al., 2017; Vinyals et al., 2016) which learns to adapt to classes. However, in this paper, we find that there new tasks sampled from unseen data with the same distribution exist high similarities between NCC-learned representations as seen data, cross-domain few-shot classification of two samples from different classes. is a much more challenging learning task since there exist In order to address this problem, we propose a discrepancies between the distributions of source and target bi-level optimization framework, maximizing optimized domains (Chi et al., 2021; Kuzborskij & Orabona, 2013).

Meta-learning is a powerful paradigm for few-shot learning. Although with remarkable success witnessed in many applications, the existing optimization based meta-learning models with over-parameterized neural networks have been evidenced to ovetfit on training tasks. To remedy this deficiency, we propose a network pruning based meta-learning approach for overfitting reduction via explicitly controlling the capacity of network. A uniform concentration analysis reveals the benefit of network capacity constraint for reducing generalization gap of the proposed meta-learner. We have implemented our approach on top of Reptile assembled with two network pruning routines: Dense-Sparse-Dense (DSD) and Iterative Hard Thresholding (IHT). Extensive experimental results on benchmark datasets with different over-parameterized deep networks demonstrate that our method not only effectively alleviates meta-overfitting but also in many cases improves the overall generalization performance when applied to few-shot classification tasks.