153 citations · 340 across the 17 of their papers we have counts for
17 papers
Robust Dynamic Expansion for Continual Learning under Backdoor Attacks via Purification and Selective Recovery
Keyu Lin, Fei Ye, Qihe Liu +2
Continual learning (CL) enables models to acquire new knowledge from sequentially arriving tasks while retaining previously learned knowledge. However, in practical scenarios, task…
Layered Rendering Diffusion Model for Controllable Zero-Shot Image Synthesis
Zipeng Qi, Guoxi Huang, Chenyang Liu +1
This paper introduces innovative solutions to enhance spatial controllability in diffusion models reliant on text queries. We first introduce vision guidance as a foundational spat…
Learning Harmonic Molecular Representations on Riemannian Manifold
Yiqun Wang, Yuning Shen, Shi Chen +3
Molecular representation learning plays a crucial role in AI-assisted drug discovery research. Encoding 3D molecular structures through Euclidean neural networks has become the pre…
Task-Free Continual Learning via Online Discrepancy Distance Learning
Fei Ye, Adrian G. Bors
Learning from non-stationary data streams, also called Task-Free Continual Learning (TFCL) remains challenging due to the absence of explicit task information. Although recently so…
Continual Variational Autoencoder Learning via Online Cooperative Memorization
Fei Ye, Adrian G. Bors
Due to their inference, data representation and reconstruction properties, Variational Autoencoders (VAE) have been successfully used in continual learning classification tasks. Ho…
Learning an evolved mixture model for task-free continual learning
Fei Ye, Adrian G. Bors
Recently, continual learning (CL) has gained significant interest because it enables deep learning models to acquire new knowledge without forgetting previously learnt information.…
Lifelong Generative Modelling Using Dynamic Expansion Graph Model
Fei Ye, Adrian G. Bors
Variational Autoencoders (VAEs) suffer from degenerated performance, when learning several successive tasks. This is caused by catastrophic forgetting. In order to address the know…
Lifelong Infinite Mixture Model Based on Knowledge-Driven Dirichlet Process
Fei Ye, Adrian G. Bors
Recent research efforts in lifelong learning propose to grow a mixture of models to adapt to an increasing number of tasks. The proposed methodology shows promising results in over…
InfoVAEGAN : learning joint interpretable representations by information maximization and maximum likelihood
Fei Ye, Adrian G. Bors
Learning disentangled and interpretable representations is an important step towards accomplishing comprehensive data representations on the manifold. In this paper, we propose a n…
Lifelong Mixture of Variational Autoencoders
Fei Ye, Adrian G. Bors
In this paper, we propose an end-to-end lifelong learning mixture of experts. Each expert is implemented by a Variational Autoencoder (VAE). The experts in the mixture system are j…
Lifelong Teacher-Student Network Learning
Fei Ye, Adrian G. Bors
A unique cognitive capability of humans consists in their ability to acquire new knowledge and skills from a sequence of experiences. Meanwhile, artificial intelligence systems are…
Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework
Fei Ye, Huangjie Zheng, Chaoqin Huang +1
Surrogate task based methods have recently shown great promise for unsupervised image anomaly detection. However, there is no guarantee that the surrogate tasks share the consisten…
ESAD: End-to-end Deep Semi-supervised Anomaly Detection
Chaoqin Huang, Fei Ye, Peisen Zhao +3
This paper explores semi-supervised anomaly detection, a more practical setting for anomaly detection where a small additional set of labeled samples are provided. We propose a new…
Learning latent representations across multiple data domains using Lifelong VAEGAN
Fei Ye, Adrian G. Bors
The problem of catastrophic forgetting occurs in deep learning models trained on multiple databases in a sequential manner. Recently, generative replay mechanisms (GRM), have been…
Few-Shot Bearing Fault Diagnosis Based on Model-Agnostic Meta-Learning
Shen Zhang, Fei Ye, Bingnan Wang +1
The rapid development of artificial intelligence and deep learning has provided many opportunities to further enhance the safety, stability, and accuracy of industrial Cyber-Physic…
Semi-Supervised Learning of Bearing Anomaly Detection via Deep Variational Autoencoders
Shen Zhang, Fei Ye, Bingnan Wang +1
Most of the data-driven approaches applied to bearing fault diagnosis up to date are established in the supervised learning paradigm, which usually requires a large set of labeled…
Attribute Restoration Framework for Anomaly Detection
Chaoqin Huang, Fei Ye, Jinkun Cao +3
With the recent advances in deep neural networks, anomaly detection in multimedia has received much attention in the computer vision community. While reconstruction-based methods h…