论文标题
优化图像嵌入式的射击学习
Optimization of Image Embeddings for Few Shot Learning
论文作者
论文摘要
在本文中，我们改善了图神经网络解决方案中生成的图像嵌入，以进行一些射击学习。我们为现有网络（例如Inception-NET，U-NET，注意U-NET和Squeeze-NET）提出替代体系结构，以生成嵌入并提高模型的准确性。我们以产生它们的时间为代价创建的嵌入质量。所提出的实现优于Omniglot数据集上的1次和5次学习的现有最新方法。实验涉及测试集和训练集，它们之间没有共同的类别。列出了5路和10路测试的结果。
In this paper we improve the image embeddings generated in the graph neural network solution for few shot learning. We propose alternate architectures for existing networks such as Inception-Net, U-Net, Attention U-Net, and Squeeze-Net to generate embeddings and increase the accuracy of the models. We improve the quality of embeddings created at the cost of the time taken to generate them. The proposed implementations outperform the existing state of the art methods for 1-shot and 5-shot learning on the Omniglot dataset. The experiments involved a testing set and training set which had no common classes between them. The results for 5-way and 10-way/20-way tests have been tabulated.
