2023
CVPR
CVPR 2023
Exploring and Utilizing Pattern Imbalance
Abstract
In this paper, we identify pattern imbalance from several aspects, and further develop a new training scheme to avert pattern preference as well as spurious correlation. In contrast to prior methods which are mostly concerned with category or domain granularity, ignoring the potential finer structure that existed in datasets, we give a new definition of seed category as an appropriate optimization unit to distinguish different patterns in the same category or domain. Extensive experiments on domain generalization datasets of diverse scales demonstrate the effectiveness of the proposed method.
Authors
Topics
Artificial Intelligence > Learning Paradigms > Transfer Learning
Machine Learning > Core Methods > Classification
Machine Learning > Core Methods > Representation Learning
Machine Learning > Application Areas > Domain Generalization
Machine Learning > Learning Types > Multi-Task Learning
Machine Learning > Learning Types > Supervised Learning
Deep Learning > Learning Types > Domain Adaptation