54 citations · 77 across the 24 of their papers we have counts for
6 papers · 1 filter
Accelerating Visual On-Policy Distillation with Batched Speculative Jacobi Rollouts
Bingqi Shan, Zhehao Yu, Kenhong Lin +1
Visual on-policy distillation (OPD) improves the training of compact visual autoregressive models by learning from trajectories generated by the current student. However, these onl…
GenFT: A Generative Parameter-Efficient Fine-Tuning Method for Pretrained Foundation Models
Guangning Xu, Baoquan Zhang, Michael. K. Ng
Parameter-efficient fine-tuning (PEFT) has emerged as a resource-efficient strategy for adapting Pretrained Foundation Models (PFMs) by learning a small number of task-specific upd…
UER: A Heuristic Bias Addressing Approach for Online Continual Learning
Huiwei Lin, Shanshan Feng, Baoquan Zhang +3
Online continual learning aims to continuously train neural networks from a continuous data stream with a single pass-through data. As the most effective approach, the rehearsal-ba…
HPCR: Holistic Proxy-based Contrastive Replay for Online Continual Learning
Huiwei Lin, Shanshan Feng, Baoquan Zhang +2
Online continual learning, aimed at developing a neural network that continuously learns new data from a single pass over an online data stream, generally suffers from catastrophic…
MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning
Baoquan Zhang, Chuyao Luo, Demin Yu +4
Equipping a deep model the abaility of few-shot learning, i.e., learning quickly from only few examples, is a core challenge for artificial intelligence. Gradient-based meta-learni…
MetaDT: Meta Decision Tree with Class Hierarchy for Interpretable Few-Shot Learning
Baoquan Zhang, Hao Jiang, Xutao Li +3
Few-Shot Learning (FSL) is a challenging task, which aims to recognize novel classes with few examples. Recently, lots of methods have been proposed from the perspective of meta-le…