no code implementations • 9 Nov 2023 • Qinghua Lin, Zuoyong Li, Kun Zeng, Haoyi Fan, Wei Li, Xiaoguang Zhou
Considering the limited quantity of labeled video data, we propose a semi-supervised fire detection model called FireMatch, which is based on consistency regularization and adversarial distribution alignment.
1 code implementation • 1 Sep 2021 • Fengbin Zhang, Haoyi Fan, Ruidong Wang, Zuoyong Li, Tiancai Liang
In this paper, we propose an end-to-end model of Deep Dual Support Vector Data description based Autoencoder (Dual-SVDAE) for anomaly detection on attributed networks, which considers both the structure and attribute for attributed networks.
1 code implementation • ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2021 • Haoyi Fan, Fengbin Zhang, Ruidong Wang, Xunhua Huang, and Zuoyong Li
Then, the temporal relation between those segments is predicted by SemiTime in a self-supervised manner.
1 code implementation • 27 Nov 2020 • Haoyi Fan, Fengbin Zhang, Yue Gao
In this paper, we present SelfTime: a general self-supervised time series representation learning framework, by exploring the inter-sample relation and intra-temporal relation of time series to learn the underlying structure feature on the unlabeled time series.
1 code implementation • 18 Feb 2020 • Haoyi Fan, Fengbin Zhang, Ruidong Wang, Liang Xi, Zuoyong Li
Unsupervised anomaly detection aims to identify anomalous samples from highly complex and unstructured data, which is pervasive in both fundamental research and industrial applications.
3 code implementations • 10 Feb 2020 • Haoyi Fan, Fengbin Zhang, Zuoyong Li
In this paper, we propose a deep joint representation learning framework for anomaly detection through a dual autoencoder (AnomalyDAE), which captures the complex interactions between network structure and node attribute for high-quality embeddings.