765 citations · 1.6k across the 39 of their papers we have counts for
7 papers · 1 filter
Disentangled Representation Learning
Xin Wang, Hong Chen, Si'ao Tang +2
Disentangled Representation Learning (DRL) aims to learn a model capable of identifying and disentangling the underlying factors hidden in the observable data in representation for…
Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training
Hongmin Cai, Wenxiong Liao, Zhengliang Liu +12
Modern supervised learning neural network models require a large amount of manually labeled data, which makes the construction of domain-specific knowledge graphs time-consuming an…
Is Multi-Task Learning an Upper Bound for Continual Learning?
Zihao Wu, Huy Tran, Hamed Pirsiavash +1
Continual and multi-task learning are common machine learning approaches to learning from multiple tasks. The existing works in the literature often assume multi-task learning as a…
Coupling Visual Semantics of Artificial Neural Networks and Human Brain Function via Synchronized Activations
Lin Zhao, Haixing Dai, Zihao Wu +8
Artificial neural networks (ANNs), originally inspired by biological neural networks (BNNs), have achieved remarkable successes in many tasks such as visual representation learning…
Eye-gaze-guided Vision Transformer for Rectifying Shortcut Learning
Chong Ma, Lin Zhao, Yuzhong Chen +15
Learning harmful shortcuts such as spurious correlations and biases prevents deep neural networks from learning the meaningful and useful representations, thus jeopardizing the gen…
A Unified and Biologically-Plausible Relational Graph Representation of Vision Transformers
Yuzhong Chen, Yu Du, Zhenxiang Xiao +8
Vision transformer (ViT) and its variants have achieved remarkable successes in various visual tasks. The key characteristic of these ViT models is to adopt different aggregation s…
Mask-guided Vision Transformer (MG-ViT) for Few-Shot Learning
Yuzhong Chen, Zhenxiang Xiao, Lin Zhao +10
Learning with little data is challenging but often inevitable in various application scenarios where the labeled data is limited and costly. Recently, few-shot learning (FSL) gaine…