论文标题
生成代表样本以进行几次射击分类
Generating Representative Samples for Few-Shot Classification
论文作者
论文摘要
几乎没有射击学习（FSL）旨在学习每个课程的几个视觉样本的新类别。由于数据稀缺性，通常很少有类似的班级表示。为了减轻此问题，我们建议使用条件变分自动编码器（CVAE）模型根据语义嵌入生成视觉样本。我们在基本类别上训练此CVAE模型，并使用它来生成新课程的功能。更重要的是，我们指导该VAE通过在训练CVAE模型时从基础训练集中删除非代表性样本来严格生成代表性样本。我们表明，该训练方案增强了生成样品的代表性，因此可以改善少量射击分类结果。实验结果表明，我们的方法通过大量利润来改善三种FSL基线方法，从而实现了最先进的微型成员和Tieredimagenet数据集的最先进的分类性能。代码可在以下网址获得：https：//github.com/cvlab-stonybrook/fsl-rsvae。
Few-shot learning (FSL) aims to learn new categories with a few visual samples per class. Few-shot class representations are often biased due to data scarcity. To mitigate this issue, we propose to generate visual samples based on semantic embeddings using a conditional variational autoencoder (CVAE) model. We train this CVAE model on base classes and use it to generate features for novel classes. More importantly, we guide this VAE to strictly generate representative samples by removing non-representative samples from the base training set when training the CVAE model. We show that this training scheme enhances the representativeness of the generated samples and therefore, improves the few-shot classification results. Experimental results show that our method improves three FSL baseline methods by substantial margins, achieving state-of-the-art few-shot classification performance on miniImageNet and tieredImageNet datasets for both 1-shot and 5-shot settings. Code is available at: https://github.com/cvlab-stonybrook/fsl-rsvae.
