Unsupervised Learning
Unsupervised learning aims to extract meaningful patterns and structures from unlabeled data, addressing the limitations of supervised methods that require extensive labeled datasets. Current research focuses on developing robust algorithms and model architectures, such as autoencoders, generative models (like Gaussian Mixture Models), and contrastive learning approaches, to improve clustering, anomaly detection, and representation learning. These advancements are impacting diverse fields, including medical image analysis, financial market prediction, and signal processing, by enabling efficient analysis of large, unlabeled datasets and reducing reliance on expensive manual labeling.
Papers
SiNC+: Adaptive Camera-Based Vitals with Unsupervised Learning of Periodic Signals
Jeremy Speth, Nathan Vance, Patrick Flynn, Adam Czajka
SSVT: Self-Supervised Vision Transformer For Eye Disease Diagnosis Based On Fundus Images
Jiaqi Wang, Mengtian Kang, Yong Liu, Chi Zhang, Ying Liu, Shiming Li, Yue Qi, Wenjun Xu, Chenyu Tang, Edoardo Occhipinti, Mayinuer Yusufu, Ningli Wang, Weiling Bai, Shuo Gao, Luigi G. Occhipinti
MaskFi: Unsupervised Learning of WiFi and Vision Representations for Multimodal Human Activity Recognition
Jianfei Yang, Shijie Tang, Yuecong Xu, Yunjiao Zhou, Lihua Xie
Unsupervised Learning of High-resolution Light Field Imaging via Beam Splitter-based Hybrid Lenses
Jianxin Lei, Chengcai Xu, Langqing Shi, Junhui Hou, Ping Zhou