19 citations · 57 across the 20 of their papers we have counts for
12 papers · 1 filter
DESIRE: Dynamic Knowledge Consolidation for Rehearsal-Free Continual Learning
Haiyang Guo, Fei Zhu, Fanhu Zeng +2
Continual learning aims to equip models with the ability to retain previously learned knowledge like a human. Recent work incorporating Parameter-Efficient Fine-Tuning has revitali…
Happy: A Debiased Learning Framework for Continual Generalized Category Discovery
Shijie Ma, Fei Zhu, Zhun Zhong +3
Constantly discovering novel concepts is crucial in evolving environments. This paper explores the underexplored task of Continual Generalized Category Discovery (C-GCD), which aim…
Local-Prompt: Extensible Local Prompts for Few-Shot Out-of-Distribution Detection
Fanhu Zeng, Zhen Cheng, Fei Zhu +2
Out-of-Distribution (OOD) detection, aiming to distinguish outliers from known categories, has gained prominence in practical scenarios. Recently, the advent of vision-language mod…
PASS++: A Dual Bias Reduction Framework for Non-Exemplar Class-Incremental Learning
Fei Zhu, Xu-Yao Zhang, Zhen Cheng +1
Class-incremental learning (CIL) aims to recognize new classes incrementally while maintaining the discriminability of old classes. Most existing CIL methods are exemplar-based, i.…
MSPE: Multi-Scale Patch Embedding Prompts Vision Transformers to Any Resolution
Wenzhuo Liu, Fei Zhu, Shijie Ma +1
Although Vision Transformers (ViTs) have recently advanced computer vision tasks significantly, an important real-world problem was overlooked: adapting to variable input resolutio…
Multi-scale Unified Network for Image Classification
Wenzhuo Liu, Fei Zhu, Cheng-Lin Liu
Convolutional Neural Networks (CNNs) have advanced significantly in visual representation learning and recognition. However, they face notable challenges in performance and computa…
Towards Non-Exemplar Semi-Supervised Class-Incremental Learning
Wenzhuo Liu, Fei Zhu, Cheng-Lin Liu
Deep neural networks perform remarkably well in close-world scenarios. However, novel classes emerged continually in real applications, making it necessary to learn incrementally.…
Branch-Tuning: Balancing Stability and Plasticity for Continual Self-Supervised Learning
Wenzhuo Liu, Fei Zhu, Cheng-Lin Liu
Self-supervised learning (SSL) has emerged as an effective paradigm for deriving general representations from vast amounts of unlabeled data. However, as real-world applications co…
Active Generalized Category Discovery
Shijie Ma, Fei Zhu, Zhun Zhong +2
Generalized Category Discovery (GCD) is a pragmatic and challenging open-world task, which endeavors to cluster unlabeled samples from both novel and old classes, leveraging some l…
Revisiting Confidence Estimation: Towards Reliable Failure Prediction
Fei Zhu, Xu-Yao Zhang, Zhen Cheng +1
Reliable confidence estimation is a challenging yet fundamental requirement in many risk-sensitive applications. However, modern deep neural networks are often overconfident for th…
Open-world machine learning: A review and new outlooks
Fei Zhu, Shijie Ma, Zhen Cheng +4
Machine learning has achieved remarkable success in many applications. However, existing studies are largely based on the closed-world assumption, which assumes that the environmen…
PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learning
Haiyang Guo, Fei Zhu, Wenzhuo Liu +2
Existing federated learning methods have effectively dealt with decentralized learning in scenarios involving data privacy and non-IID data. However, in real-world situations, each…