Skip to yearly menu bar Skip to main content


Poster

MetaReg: Towards Domain Generalization using Meta-Regularization

Yogesh Balaji · Swami Sankaranarayanan · Rama Chellappa

Room 517 AB #157

Keywords: [ Deep Learning ] [ Computer Vision ] [ Multitask and Transfer Learning ] [ Representation Learning ] [ Meta-Learning ]


Abstract:

Training models that generalize to new domains at test time is a problem of fundamental importance in machine learning. In this work, we encode this notion of domain generalization using a novel regularization function. We pose the problem of finding such a regularization function in a Learning to Learn (or) meta-learning framework. The objective of domain generalization is explicitly modeled by learning a regularizer that makes the model trained on one domain to perform well on another domain. Experimental validations on computer vision and natural language datasets indicate that our method can learn regularizers that achieve good cross-domain generalization.

Live content is unavailable. Log in and register to view live content