Domain generalization based on data representation invariance
Domain generalization has become a prominent research direction in artificial intelligence,aiming to learn task-related invariant representations from different data distributions.It seeks to remove the impact of varying domains on learning tasks,thereby enhancing the model's domain generalization capabilities.Based on the idea of minimizing the risk of invariance,this paper divided neural networks into feature extractors and invariance classifiers for exploration.For the feature extractor,a group whitening method based on Newtonian iteration was utilized to control the distribution of activation values.This allowed different images to remove part of the domain information after passing through the neural network,thus achieving the purpose of domain generalization.For the invariance classifier,the effects of the normalization method of features and weights on the generalization effect of the model domain were explored,and a snowflake algorithm based on the cosine similarity loss function was proposed.This algorithm improved the accuracy of model domain generalization.In addition,extensive theoretical derivations about the snowflake algorithm and in-depth analyses were provided,offering sufficient theoretical support for the experiment.
NETL
NSTL
万方数据
