CROSS-LEVEL DISTILLATION AND FEATURE DENOIS-ING FOR CROSS-DOMAIN FEW-SHOT CLASSIFICATION

Abstract

The conventional few-shot classification aims at learning a model on a large labeled base dataset and rapidly adapting to a target dataset that is from the same distribution as the base dataset. However, in practice, the base and the target datasets of few-shot classification are usually from different domains, which is the problem of cross-domain few-shot classification. We tackle this problem by making a small proportion of unlabeled images in the target domain accessible in the training stage. In this setup, even though the base data are sufficient and labeled, the large domain shift still makes transferring the knowledge from the base dataset difficult. We meticulously design a cross-level knowledge distillation method, which can strengthen the ability of the model to extract more discriminative features in the target dataset by guiding the network's shallow layers to learn higher-level information. Furthermore, in order to alleviate the overfitting in the evaluation stage, we propose a feature denoising operation which can reduce the feature redundancy and mitigate overfitting. Our approach can surpass the previous state-of-the-art method, Dynamic-Distillation, by 5.44% on 1-shot and 1.37% on 5-shot classification tasks on average in the BSCD-FSL benchmark. The implementation code will be available at

1. INTRODUCTION

Deep learning has achieved great success on image recognition tasks with the help of a large number of labeled images. However, it is the exact opposite of the human perception mechanism which can recognize a new category by learning only a few samples. Besides, a large amount of annotations is costly and unavailable for some scenarios. It is more valuable to study few-shot classification which trains a classification model on a base dataset and rapidly adapts it to the target dataset. However, due to the constraint that the base data and the target data need to be consistent in their distributions, the conventional few-shot classification may not cater to the demands in some practical scenarios. For example, it may fail in scenarios where the training domain is natural images, but the evaluation domain is satellite images. Considering this domain shift in practical applications, we focus on cross-domain few-shot classification (CD-FSC) in this paper. Previous methods, such as (Mangla et al., 2020; Adler et al., 2020; Tseng et al., 2020) , can handle this problem with small domain gaps. However, the CD-FSC problem with a large domain gap is still a challenge. BSCD-FSC (Guo et al., 2020) is a suitable benchmark for studying this problem, where the base dataset has natural images and the target datasets contain satellite images, crop disease images, skin disease images and X-ray images of sundry lung diseases. On this benchmark, previous methods following the traditional CD-FSC protocol train their models on the base dataset and evaluate them

funding

research/cv/CLDFD.

availability

https://gitee.com/mindspore/models/tree/master/

