The Development of the China Digital Library

This article describes the China Digital Library (CDL) and explains how to use the computer network in China. It also explores the advantages of the China library in establishing the CDL. Finally, it examines the future construction and development plans for the CDL. China is a large country that contains 5% of the world’s population. Along with the rapid development of the Internet worldwide, China has advanced her steps. The digital library has become a focal point in the high-tech world. At the same time, it is also a very important symbol with which to evaluate the basic facilities of information sciences in a country. The leaders in library fields have done a great deal to play important roles in realizing the China Digital Library (CDL)

Unsupervised Domain Adaptation via Deep Hierarchical Optimal Transport

Unsupervised domain adaptation is a challenging task that aims to estimate a transferable model for unlabeled target domain by exploiting source labeled data. Optimal Transport (OT) based methods recently have been proven to be a promising direction for domain adaptation due to their competitive performance. However, most of these methods coarsely aligned source and target distributions, leading to the over-aligned problem where the category-discriminative information is mixed up although domain-invariant representations can be learned. In this paper, we propose a Deep Hierarchical Optimal Transport method (DeepHOT) for unsupervised domain adaptation. The main idea is to use hierarchical optimal transport to learn both domain-invariant and category-discriminative representations by mining the rich structural correlations among domain data. The DeepHOT framework consists of a domain-level OT and an image-level OT, where the latter is used as the ground distance metric for the former. The image-level OT captures structural associations of local image regions that are beneficial to image classification, while the domain-level OT learns domain-invariant representations by leveraging the underlying geometry of domains. However, due to the high computational complexity, the optimal transport based models are limited in some scenarios. To this end, we propose a robust and efficient implementation of the DeepHOT framework by approximating origin OT with sliced Wasserstein distance in image-level OT and using a mini-batch unbalanced optimal transport for domain-level OT. Extensive experiments show that DeepHOT surpasses the state-of-the-art methods in four benchmark datasets. Code will be released on GitHub.Comment: 9 pages, 3 figure