768 citations · 874 across the 8 of their papers we have counts for
15 papers
Cross-Domain Few-Shot Classification via Adversarial Task Augmentation
Haoqing Wang, Zhi-Hong Deng
Few-shot classification aims to recognize unseen classes with few labeled samples from each class. Many meta-learning models for few-shot classification elaborately design various…
Few-shot Learning with LSSVM Base Learner and Transductive Modules
Haoqing Wang, Zhi-Hong Deng
The performance of meta-learning approaches for few-shot learning generally depends on three aspects: features suitable for comparison, the classifier ( base learner ) suitable for…
Self-Supervised Learning Aided Class-Incremental Lifelong Learning
Song Zhang, Gehui Shen, Jinsong Huang +1
Lifelong or continual learning remains to be a challenge for artificial neural network, as it is required to be both stable for preservation of old knowledge and plastic for acquis…
Generative Feature Replay with Orthogonal Weight Modification for Continual Learning
Gehui Shen, Song Zhang, Xiang Chen +1
The ability of intelligent agents to learn and remember multiple tasks sequentially is crucial to achieving artificial general intelligence. Many continual learning (CL) methods ha…
Fast Structured Decoding for Sequence Models
Zhiqing Sun, Zhuohan Li, Haoqing Wang +3
Autoregressive sequence models achieve state-of-the-art performance in domains like machine translation. However, due to the autoregressive factorization nature, these models suffe…
Dynamically Pruned Message Passing Networks for Large-Scale Knowledge Graph Reasoning
Xiaoran Xu, Wei Feng, Yunsheng Jiang +3
We propose Dynamically Pruned Message Passing Networks (DPMPN) for large-scale knowledge graph reasoning. In contrast to existing models, embedding-based or path-based, we learn an…