no code implementations • 23 Nov 2019 • Pinzhuo Tian, Zhangkai Wu, Lei Qi, Lei Wang, Yinghuan Shi, Yang Gao
To address the annotation scarcity issue in some cases of semantic segmentation, there have been a few attempts to develop the segmentation model in the few-shot learning paradigm.
1 code implementation • 1 Jan 2023 • Zhangkai Wu, Longbing Cao, Lei Qi
VAEs still suffer from uncertain tradeoff learning. We propose a novel evolutionary variational autoencoder (eVAE) building on the variational information bottleneck (VIB) theory and integrative evolutionary neural learning.
no code implementations • 23 Sep 2023 • Zhangkai Wu, Longbing Cao
Then, a self-supervised contrastive classifier differentiates the disentangled representations from the coupled representations, where a contrastive loss regularizes this contrastive classification together with the TC loss for eliminating entangled factors and strengthening disentangled representations.
no code implementations • 7 Jan 2024 • Zhangkai Wu, Longbing Cao, Qi Zhang, Junxian Zhou, Hui Chen
Due to their unsupervised training and uncertainty estimation, deep Variational Autoencoders (VAEs) have become powerful tools for reconstruction-based Time Series Anomaly Detection (TSAD).
no code implementations • 18 Feb 2024 • Yakun Chen, Kaize Shi, Zhangkai Wu, Juan Chen, Xianzhi Wang, Julian McAuley, Guandong Xu, Shui Yu
Spatiotemporal data analysis is pivotal across various domains, such as transportation, meteorology, and healthcare.
no code implementations • 17 Mar 2024 • Hanze Ding, Zhangkai Wu, Jiyan Zhang, Ming Ping, Yanfang Liu
Since the relation structure of local features embedded in graph space will help to eliminate \textit{Semantic Difference}, we employ Multi-Prototype Reasoning (MPR) module, extracting local descriptors based prototypes and analyzing local-view feature relevance in support-query pairs.
no code implementations • 24 Apr 2024 • Hui Chen, Hengyu Liu, Zhangkai Wu, Xuhui Fan, Longbing Cao
While deep neural networks (DNNs) based personalized federated learning (PFL) is demanding for addressing data heterogeneity and shows promising performance, existing methods for federated learning (FL) suffer from efficient systematic uncertainty quantification.