In this paper, we look at cross-domain few-shot classification which presents
the challenging task of learning new classes in unseen domains with few
labelled examples. Existing methods, though somewhat effective, encounter
several limitations, which we address in this work through two significant
improvements. First, to address overfitting associated with fine-tuning a large
number of parameters on small datasets, we introduce a lightweight
parameter-efficient adaptation strategy. This strategy employs a linear
transformation of pre-trained features, significantly reducing the trainable
parameter count. Second, we replace the traditional nearest centroid classifier
with a variance-aware loss function, enhancing the model's sensitivity to the
inter- and intra-class variances within the training set for improved
clustering in feature space. Empirical evaluations on the Meta-Dataset
benchmark showcase that our approach not only improves accuracy up to 7.7% and
5.3% on seen and unseen datasets respectively but also achieves this
performance while being at least ~3x more parameter-efficient than existing
methods, establishing a new state-of-the-art in cross-domain few-shot learning.
Our code can be found at this https URL

本文研究跨领域少样本分类，通过引入轻量级参数适配策略和方差感知损失函数来提高模型性能，在 Meta-Dataset 基准测试中，相较于现有方法，准确率分别提高了 7.7% 和 5.3%，且参数效率至少提高 3 倍，达到了新的国际水平。