Domain Generalization Meta Learning

In terms of learning with multiple domains a few studies 21 3 11 have considered meta learning for multi source domain generalization which evaluates the ability of models to generalise.

Domain generalization meta learning. Meta learning for domain generalization. Recently finn et al. Methodology meta learning domain generalization in the dg setting we assume there are ssource domains sand t target domains t.

An attempt to replicate the paper learning to generalize. Deeper broader and artier domain generalization. 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.

Meta learning 40 51 is a long stand ing topic in how to learn new concepts or tasks fast with a few training examples. Domain generalization dg techniques attempt to alleviate this issue by producing models which by design generalize well to novel testing domains. 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. Meta learning for domain generalization. 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.

We propose a novel meta learning method for domain generalization. Mldg folder contains the code for the meta learning approach. A new meta learning objective based around simulating do main shift and training such that steps to improve the source domain also improve the simulated testing domains.

Ing to improve single domain generalization. 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. 2 da li yongxin yang yi zhe song and timothy m hospedales.