Domain Generalization Machine Learning

Domain shift refers to the well known problem that a model trained in one source domain performs poorly when applied to a target domain with different statistics.

Domain generalization machine learning. In this work we encode this notion of domain generalization using a novel regularization function. The goal of domain generalization is to learn from multiple source domains to generalize to unseen target domains under distribution discrepancy. Proceedings of the ieee conference on computer vision and pattern recognition cvpr 2020.

A machine learning algorithm is used to fit a model to data. With supervised learning a set of labeled training data is given to a model. In this paper we propose the first method of domain generalization to leverage unlabeled.

Training the model is kind of like infancy for humans. To answer supervised learning in the domain of machine learning refers to a way for the model to learn and understand data. Learning to learn single domain generalization fengchun qiao long zhao xi peng.

We investigate the challenging problem of domain generalization i e training a model on multi domain source data such that it can directly generalize to target domains with unknown statistics. The objective of domain generalization is explicitly modeled. Generalization capability to unseen domains is crucial for machine learning models when deploying to real world conditions.

Fundamental importance in machine learning. Proceedings of the ieee conference on computer vision and pattern recognition cvpr 2020. We pose the problem of finding such a regularization function in a learning to learn or meta learning framework.

Before talking about generalization in machine learning it s important to first understand what supervised learning is. Learning to learn single domain generalization fengchun qiao long zhao xi peng. Examples are presented to the model and the model tweaks its internal parameters to better understand the data.