Generative Dual Adversarial Network for Generalized Zero-Shot Learning
Generative Dual Adversarial Network for Generalized Zero-Shot Learning
复制标题
DOI：
10.1109/cvpr.2019.00089
复制
发表时间：
2018-11
期刊：
影响因子：
--
通讯作者：
He Huang;Chang-Dong Wang;Philip S. Yu;Chang-Dong Wang
中科院分区：
文献类型：
--
作者：
He Huang;Chang-Dong Wang;Philip S. Yu;Chang-Dong Wang
本文研究了广义零样本学习问题，该问题要求模型对一些已见类别中的图像标签对进行训练，并测试对已见类别和未见类别中的新图像进行分类的任务。在本文中，我们提出了一种新颖的模型，为三种不同的方法提供了统一的框架：视觉->语义映射、语义->视觉映射和度量学习。具体来说，我们提出的模型由一个特征生成器组成，该特征生成器可以在给定类嵌入作为输入的情况下生成各种视觉特征，一个将每个视觉特征映射回其相应的类嵌入的回归器，以及一个学习评估图像特征和类嵌入的接近程度的鉴别器。所有三个组件都在循环一致性损失和双重对抗性损失的组合下进行训练。实验结果表明，我们的模型不仅在对已见类别的图像进行分类时保持了更高的准确性，而且在对未见类别的图像进行分类时比现有最先进的模型表现更好。
This paper studies the problem of generalized zero-shot learning which requires the model to train on image-label pairs from some seen classes and test on the task of classifying new images from both seen and unseen classes. In this paper, we propose a novel model that provides a unified framework for three different approaches: visual->semantic mapping, semantic->visual mapping, and metric learning. Specifically, our proposed model consists of a feature generator that can generate various visual features given class embeddings as input, a regressor that maps each visual feature back to its corresponding class embedding, and a discriminator that learns to evaluate the closeness of an image feature and a class embedding. All three components are trained under the combination of cyclic consistency loss and dual adversarial loss. Experimental results show that our model not only preserves higher accuracy in classifying images from seen classes, but also performs better than existing state-of-the-art models in in classifying images from unseen classes.