1 citations · 3 across the 5 of their papers we have counts for
5 papers
Pushing the Limits of Distillation-Based Continual Learning via Classifier-Proximal Lightweight Plugins
Zhiming Xu, Baile Xu, Jian Zhao +2
Continual learning requires models to learn continuously while preserving prior knowledge under evolving data streams. Distillation-based methods are appealing for retaining past k…
RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment
Suorong Yang, Peijia Li, Furao Shen +1
Modern deep architectures often rely on large-scale datasets, but training on these datasets incurs high computational and storage overhead. Real-world datasets often contain subst…
EntAugment: Entropy-Driven Adaptive Data Augmentation Framework for Image Classification
Suorong Yang, Furao Shen, Jian Zhao
Data augmentation (DA) has been widely used to improve the generalization of deep neural networks. While existing DA methods have proven effective, they often rely on augmentation…
AdaAugment: A Tuning-Free and Adaptive Approach to Enhance Data Augmentation
Suorong Yang, Peijia Li, Xin Xiong +2
Data augmentation (DA) is widely employed to improve the generalization performance of deep models. However, most existing DA methods employ augmentation operations with fixed or r…
Not All Data Matters: An End-to-End Adaptive Dataset Pruning Framework for Enhancing Model Performance and Efficiency
Suorong Yang, Hongchao Yang, Suhan Guo +2
While deep neural networks have demonstrated remarkable performance across various tasks, they typically require massive training data. Due to the presence of redundancies and bias…