With the recent explosive increase of digital data, image recognition and retrieval become a critical practical application. Hashing is an effective solution to this problem, due to its low storage requirement and high query speed. However, most of past works focus on hashing in a single (source) domain. Thus, the learned hash function may not adapt well in a new (target) domain that has a large distributional difference with the source domain. In this paper, we explore an end-to-end domain adaptive learning framework that simultaneously and precisely generates discriminative hash codes and classifies target domain images. Our method encodes two domains images into a semantic common space, followed by two independent generative adversarial networks arming at crosswise reconstructing two domains' images, reducing domain disparity and improving alignment in the shared space. We evaluate our framework on {four} public benchmark datasets, all of which show that our method is superior to the other state-of-the-art methods on the tasks of object recognition and image retrieval.

本文针对单一领域哈希函数无法适应新领域的问题，提出了一个端到端的领域自适应学习框架，将两个域映射到共同的语义空间，并同时生成判别性的哈希码和分类目标域图像。通过两个独立的生成对抗网络跨向重构两个域的图像，减少域的差异，提高共享空间的对齐度，实现领域自适应。实验结果表明，我们的方法在目标识别和图像检索任务上优于其他最先进的方法，适用于公共基准数据集。

多域适应哈希：具交集生成对抗网络的单一网络