148 citations · 509 across the 46 of their papers we have counts for
11 papers · 1 filter
Neural Collapse Terminus: A Unified Solution for Class Incremental Learning and Its Variants
Yibo Yang, Haobo Yuan, Xiangtai Li +6
How to enable learnability for new classes while keeping the capability well on old classes has been a crucial challenge for class incremental learning. Beyond the normal case, lon…
Pseudo Contrastive Learning for Graph-based Semi-supervised Learning
Weigang Lu, Ziyu Guan, Wei Zhao +5
Pseudo Labeling is a technique used to improve the performance of semi-supervised Graph Neural Networks (GNNs) by generating additional pseudo-labels based on confident predictions…
A Comprehensive Survey of Dataset Distillation
Shiye Lei, Dacheng Tao
Deep learning technology has developed unprecedentedly in the last decade and has become the primary choice in many application domains. This progress is mainly attributed to a sys…
Responsible Active Learning via Human-in-the-loop Peer Study
Yu-Tong Cao, Jingya Wang, Baosheng Yu +1
Active learning has been proposed to reduce data annotation efforts by only manually labelling representative data samples for training. Meanwhile, recent active learning applicati…
Knowledge-Aware Federated Active Learning with Non-IID Data
Yu-Tong Cao, Ye Shi, Baosheng Yu +2
Federated learning enables multiple decentralized clients to learn collaboratively without sharing the local training data. However, the expensive annotation cost to acquire data l…
PAD-Net: An Efficient Framework for Dynamic Networks
Shwai He, Liang Ding, Daize Dong +3
Dynamic networks, e.g., Dynamic Convolution (DY-Conv) and the Mixture of Experts (MoE), have been extensively explored as they can considerably improve the model's representation p…
Domain-Specific Risk Minimization for Out-of-Distribution Generalization
Yi-Fan Zhang, Jindong Wang, Jian Liang +5
Recent domain generalization (DG) approaches typically use the hypothesis learned on source domains for inference on the unseen target domain. However, such a hypothesis can be arb…
Online Continual Learning with Contrastive Vision Transformer
Zhen Wang, Liu Liu, Yajing Kong +2
Online continual learning (online CL) studies the problem of learning sequential tasks from an online data stream without task boundaries, aiming to adapt to new data while allevia…
Parameter-Efficient and Student-Friendly Knowledge Distillation
Jun Rao, Xv Meng, Liang Ding +2
Knowledge distillation (KD) has been extensively employed to transfer the knowledge from a large teacher model to the smaller students, where the parameters of the teacher are fixe…
SkipNode: On Alleviating Performance Degradation for Deep Graph Convolutional Networks
Weigang Lu, Yibing Zhan, Binbin Lin +6
Graph Convolutional Networks (GCNs) suffer from performance degradation when models go deeper. However, earlier works only attributed the performance degeneration to over-smoothing…
Leveraged Matrix Completion with Noise
Xinjian Huang, Weiwei Liu, Bo Du +1
Completing low-rank matrices from subsampled measurements has received much attention in the past decade. Existing works indicate that datums are require…