7 papers
Margin-Based Transfer Bounds for Meta Learning with Deep Feature Embedding
Jiechao Guan, Zhiwu Lu, Tao Xiang +1
By transferring knowledge learned from seen/previous tasks, meta learning aims to generalize well to unseen/future tasks. Existing meta-learning approaches have shown promising emp…
Domain-Adaptive Few-Shot Learning
An Zhao, Mingyu Ding, Zhiwu Lu +5
Existing few-shot learning (FSL) methods make the implicit assumption that the few target class samples are from the same domain as the source class samples. However, in practice t…
Few-Shot Learning as Domain Adaptation: Algorithm and Analysis
Jiechao Guan, Zhiwu Lu, Tao Xiang +1
To recognize the unseen classes with only few samples, few-shot learning (FSL) uses prior knowledge learned from the seen classes. A major challenge for FSL is that the distributio…
Zero-Shot Learning with Sparse Attribute Propagation
Nanyi Fei, Jiechao Guan, Zhiwu Lu +2
Zero-shot learning (ZSL) aims to recognize a set of unseen classes without any training images. The standard approach to ZSL requires a set of training images annotated with seen c…
Zero and Few Shot Learning with Semantic Feature Synthesis and Competitive Learning
Zhiwu Lu, Jiechao Guan, Aoxue Li +3
Zero-shot learning (ZSL) is made possible by learning a projection function between a feature space and a semantic space (e.g.,~an attribute space). Key to ZSL is thus to learn a p…
Transferrable Feature and Projection Learning with Class Hierarchy for Zero-Shot Learning
Aoxue Li, Zhiwu Lu, Jiechao Guan +3
Zero-shot learning (ZSL) aims to transfer knowledge from seen classes to unseen ones so that the latter can be recognised without any training samples. This is made possible by lea…