任务相关的图像小样本深度学习分类方法研究

传统基于度量学习的图像小样本分类方法与任务无关，这导致模型对新查询任务的泛化能力 较差。针对该问题，该研究提出一种任务相关的图像小样本深度学习方法——可以根据查询任务自适 应地调整支持集样本特征，从而有效形成任务相关的度量分类器。同时，该研究通过引入多种正则化 方法，解决了数据量严重不足所带来的过拟合问题。基于 miniImageNet 和 tieredImageNet 两个常用标 准数据集，在特征提取网络相同的前提下，所提出方法在 miniImageNe 中 1-shot 上获得了 66.05% 的 准确率，较目前最好的模型提高了 4.29%。

Task-Relevant Few-Shot Image Classification

The traditional metric learning based few-shot image classification methods are task independent, which leads to poor generalization performance of the model on new query tasks. To solve this problem, a taskrelevant image few-shot learning method was proposed in this paper, which can adaptively adjust the feature of support samples according to the query task. Moreover, a variety of regularization methods to address the overfitting problem under severely-limited data scenarios were also investigated. We conduct comprehensive experiments on two popular benchmarks, i.e., miniImageNet and tieredImageNet. The result of 1-shot task on the miniImageNet by the proposed method was 66.05%, and it outperforms the SOTA (state of the art) approaches by 4.29% under the same backbones.