283 citations · 814 across the 69 of their papers we have counts for
5 papers · 1 filter
DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation
Haoyue Bai, Rui Sun, Lanqing Hong +5
While deep learning demonstrates its strong ability to handle independent and identically distributed (IID) data, it often suffers from out-of-distribution (OoD) generalization, wh…
Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks
Han-Jia Ye, Lu Han, De-Chuan Zhan
Meta-learning has become a practical approach towards few-shot image classification, where "a strategy to learn a classifier" is meta-learned on labeled base classes and can be app…
Novelty-Prepared Few-Shot Classification
Chao Wang, Ruo-Ze Liu, Han-Jia Ye +1
Few-shot classification algorithms can alleviate the data scarceness issue, which is vital in many real-world problems, by adopting models pre-trained from abundant data in other d…
Revisiting Meta-Learning as Supervised Learning
Wei-Lun Chao, Han-Jia Ye, De-Chuan Zhan +2
Recent years have witnessed an abundance of new publications and approaches on meta-learning. This community-wide enthusiasm has sparked great insights but has also created a pleth…
Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning
Han-Jia Ye, Hong-You Chen, De-Chuan Zhan +1
Classifiers trained with class-imbalanced data are known to perform poorly on test data of the "minor" classes, of which we have insufficient training data. In this paper, we inves…