Combining various training and adaptation algorithms for ensemble few-shot classification

To mitigate the shortage of labeled data, Few-Shot Classification (FSC) methods train deep neural networks (DNNs) on a base dataset with sufficient labeled data, and then adapt them to target tasks using a few labeled data. Despite notable progress, a single FSC model remains prone to high variance and low confidence. As a result, ensemble FSC has garnered increasing attention. However, the limited labeled data and the high computational cost associated with DNNs present significant challenges for ensemble FSC methods. This paper presents a novel ensemble method that generates multiple FSC models via combining various training and adaptation algorithms. Due to the reuse of training phases, the proposed method significantly reduces the learning cost while generating base models with greater diversity. To further minimize reliance on labeled data, we provide each model with pseudo-labeled data selected by the majority vote of other models. Compared with self-training style methods, this "one-vs-others" learning strategy effectively reduces pseudo-label noise and confirmation bias. Finally, we conduct extensive experiments on miniImageNet, tieredImageNet and CUB datasets. The experimental results demonstrate that our method outperforms other state-of-the-art FSC methods. Especially, our method achieves the greatest improvement in the performance of base models. The source code and related models are available at https://github.com/tn1999tn/Ensemble-FSC/tree/master.