Abundant real-world data can be naturally represented by large-scale networks, which demands efficient and effective learning algorithms. At the same time, labels may only be available for some networks, which demands these algorithms to be able to adapt to unlabeled networks. Domain-adaptive hash learning has enjoyed considerable success in the computer vision community in many practical tasks due to its lower cost in both retrieval time and storage footprint. However, it has not been applied to multiple-domain networks. In this work, we bridge this gap by developing an unsupervised domain-adaptive hash learning method for networks, dubbed UDAH. Specifically, we develop four {task-specific yet correlated} components: (1) network structure preservation via a hard groupwise contrastive loss, (2) relaxation-free supervised hashing, (3) cross-domain intersected discriminators, and (4) semantic center alignment. We conduct a wide range of experiments to evaluate the effectiveness and efficiency of our method on a range of tasks including link prediction, node classification, and neighbor recommendation. Our evaluation results demonstrate that our model achieves better performance than the state-of-the-art conventional discrete embedding methods over all the tasks.
快速相似性搜索是大规模信息检索中的关键组成部分，其中语义哈希已经成为将文档表示为二进制哈希码的流行策略。这一领域的最新进展是通过基于神经网络的模型获得的：通过学习重建原始文档来训练生成模型。我们提出了一种新的无监督生成语义哈希方法，\textit{Ranking based Semantic Hashing}（RBSH），它由一个变分和排名为基础的组件。类似于变分自编码器，变分组件被训练以根据其生成的哈希码重建原始文档，并且与先前的工作一样，它只考虑单独的文档。排名组件通过将文档间相似性并入哈希代码生成中、通过铰链损失对文档排序进行建模来解决此限制。为了避免需要标记数据来计算铰链损失，我们使用弱标记器，从而保持该方法完全无监督。针对传统基线和最近最先进的语义哈希方法对四个公开可用的数据集进行了广泛的实验评估，结果表明RBSH在所有评估的哈希码长度上都显着优于所有其他方法。事实上，RBSH哈希码能够执行类似于最先进的哈希码，同时使用2- 4倍的比特。
Optimal Projection Guided Transfer Hashing for Image Retrieval
Learning Intelligence & Vision Essential Group（学习智能与视觉基础组）；School of Microelectronics and Communication Engineering, Chongqing University, China（重庆大学微电子与通信工程学院）
Prototype-Based Semantic Consistency Alignment for Domain Adaptive Retrieval
School of Computer Science and Technology, Guangdong University of Technology（广东工业大学计算机科学与技术学院）；School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；School of Computer Science, Guangdong Polytechnic Normal University（广东 polytechnic 正规大学计算机科学学院）；School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学深圳校区计算机科学与技术学院）；School of Artificial Intelligence, Guangzhou University（广州大学人工智能学院）
Unsupervised Adaptive Neural Network Regularization for Accelerated Radial Cine MRI
Charité - Universitätsmedizin Berlin（夏里特大学医学院）；Berlin Institute of Health（柏林健康研究所）；Physikalisch-Technische Bundesanstalt (PTB)（德国联邦物理技术研究院）；King’s College London（伦敦国王学院）；Technical University of Berlin（柏林工业大学）；University of Innsbruck（因斯布鲁克大学）
