10 citations · 18 across the 10 of their papers we have counts for
10 papers
Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions
Zhenyi Wang, Li Shen, Le Fang +4
The paradigm of machine intelligence moves from purely supervised learning to a more practical scenario when many loosely related unlabeled data are available and labeled data is s…
Improving Task-free Continual Learning by Distributionally Robust Memory Evolution
Zhenyi Wang, Li Shen, Le Fang +3
Task-free continual learning (CL) aims to learn a non-stationary data stream without explicit task definitions and not forget previous knowledge. The widely adopted memory replay a…
Progressive Voronoi Diagram Subdivision: Towards A Holistic Geometric Framework for Exemplar-free Class-Incremental Learning
Chunwei Ma, Zhanghexuan Ji, Ziyun Huang +3
Exemplar-free Class-incremental Learning (CIL) is a challenging problem because rehearsing data from previous phases is strictly prohibited, causing catastrophic forgetting of Deep…
A Bayesian Detect to Track System for Robust Visual Object Tracking and Semi-Supervised Model Learning
Yan Shen, Zhanghexuan Ji, Chunwei Ma +1
Object tracking is one of the fundamental problems in visual recognition tasks and has achieved significant improvements in recent years. The achievements often come with the price…
Few-shot Learning as Cluster-induced Voronoi Diagrams: A Geometric Approach
Chunwei Ma, Ziyun Huang, Mingchen Gao +1
Few-shot learning (FSL) is the process of rapid generalization from abundant base samples to inadequate novel samples. Despite extensive research in recent years, FSL is still not…
FedMM: Saddle Point Optimization for Federated Adversarial Domain Adaptation
Yan Shen, Jian Du, Han Zhao +3
Federated adversary domain adaptation is a unique distributed minimax training task due to the prevalence of label imbalance among clients, with each client only seeing a subset of…
Meta Learning on a Sequence of Imbalanced Domains with Difficulty Awareness
Zhenyi Wang, Tiehang Duan, Le Fang +2
Recognizing new objects by learning from a few labeled examples in an evolving environment is crucial to obtain excellent generalization ability for real-world machine learning sys…
Improving Joint Learning of Chest X-Ray and Radiology Report by Word Region Alignment
Zhanghexuan Ji, Mohammad Abuzar Shaikh, Dana Moukheiber +3
Self-supervised learning provides an opportunity to explore unlabeled chest X-rays and their associated free-text reports accumulated in clinical routine without manual supervision…
LAViTeR: Learning Aligned Visual and Textual Representations Assisted by Image and Caption Generation
Mohammad Abuzar Hashemi, Zhanghexuan Li, Mihir Chauhan +5
Pre-training visual and textual representations from large-scale image-text pairs is becoming a standard approach for many downstream vision-language tasks. The transformer-based m…
Improving Uncertainty Calibration of Deep Neural Networks via Truth Discovery and Geometric Optimization
Chunwei Ma, Ziyun Huang, Jiayi Xian +2
Deep Neural Networks (DNNs), despite their tremendous success in recent years, could still cast doubts on their predictions due to the intrinsic uncertainty associated with their l…