State-of-the-art approaches in time series generation (TSG), such as TimeVQVAE, utilize vector quantization-based tokenization to effectively model complex distributions of time series. These approaches first learn to transform time series into a sequence of discrete latent vectors, and then a prior model is learned to model the sequence. The discrete latent vectors, however, only capture low-level semantics (\textit{e.g.,} shapes). We hypothesize that higher-fidelity time series can be generated by training a prior model on more informative discrete latent vectors that contain both low and high-level semantics (\textit{e.g.,} characteristic dynamics). In this paper, we introduce a novel framework, termed NC-VQVAE, to integrate self-supervised learning into those TSG methods to derive a discrete latent space where low and high-level semantics are captured. Our experimental results demonstrate that NC-VQVAE results in a considerable improvement in the quality of synthetic samples.
MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Series
Shandong University（山东大学）；Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University（山东大学-南洋理工大学联合人工智能研究中心（C-FAIR））；Lund University（隆德大学）；Nanyang Technological University（南洋理工大学）
Exploring the Coordination of Frequency and Attention in Masked Image Modeling
School of Cyber Science and Engineering, Southeast University（东南大学网络空间科学与工程学院）；Purple Mountain Laboratories（紫金山实验室）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；Alibaba Group（阿里巴巴集团）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程学系）；Zhuhai UM Science and Technology Research Institute（珠海澳大科技研究院）；Faculty of Science & Technology at University of Macau（澳门大学科技学院）
SARMAE: Masked Autoencoder for SAR Representation Learning
School of Information and Electronics, Beijing Institute of Technology, Beijing, China（北京理工大学信息与电子学院，北京，中国）；School of Computer Science, Wuhan University, Wuhan, China（武汉大学计算机学院，武汉，中国）；School of Electronic Information, Wuhan University, Wuhan, China（武汉大学电子信息学院，武汉，中国）；Zhongguancun Academy, Beijing, China（中关村学院，北京，中国）；School of Data Science, Fudan University, Shanghai, China（复旦大学数据科学学院，上海，中国）；State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China（武汉大学测绘遥感信息工程国家重点实验室，武汉，中国）
Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Model
Institute of Automation, Chinese Academy of Science（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；The University of Hong Kong（香港大学）；Tsinghua University（清华大学）；University of Science and Technology Beijing（北京科技大学）
LMAE4Eth: Generalizable and Robust Ethereum Fraud Detection by Exploring Transaction Semantics and Masked Graph Embedding
Yantai Research Institute, Harbin Engineering University（哈尔滨工程大学烟台研究院）；Hangzhou Research Institute, Xidian University（西安电子科技大学杭州研究院）；College of Computer Science, Zhejiang University（浙江大学计算机学院）
