论文标题
少数拍摄学习的代理网络
Proxy Network for Few Shot Learning
论文作者
论文摘要
每个班级使用一些示例来训练可以推广到新颖类的预测模型是人工智能的至关重要且宝贵的研究方向。这项工作通过提出了一种称为代理网络的算法（FSL）算法来解决此问题，在元学习的体系结构下。基于度量学习的方法假定同一类中的数据点应接近，而不同类中的数据点应在嵌入空间中尽可能分开。我们得出的结论是，基于度量学习的方法的成功在于嵌入数据，每个类别的代表和距离度量。在这项工作中，我们提出了一个简单但有效的端到端模型，该模型直接同时了解数据代表和距离指标的代理。我们在1-Shot-5way和5-Shot-5-way方案上对CUB和MINI-IMAGENET数据集进行了实验，实验结果证明了我们所提出的方法比最新方法的优越性。此外，我们提供了对我们提出的方法的详细分析。
The use of a few examples for each class to train a predictive model that can be generalized to novel classes is a crucial and valuable research direction in artificial intelligence. This work addresses this problem by proposing a few-shot learning (FSL) algorithm called proxy network under the architecture of meta-learning. Metric-learning based approaches assume that the data points within the same class should be close, whereas the data points in the different classes should be separated as far as possible in the embedding space. We conclude that the success of metric-learning based approaches lies in the data embedding, the representative of each class, and the distance metric. In this work, we propose a simple but effective end-to-end model that directly learns proxies for class representative and distance metric from data simultaneously. We conduct experiments on CUB and mini-ImageNet datasets in 1-shot-5-way and 5-shot-5-way scenarios, and the experimental results demonstrate the superiority of our proposed method over state-of-the-art methods. Besides, we provide a detailed analysis of our proposed method.
