506 papers with code • 19 benchmarks • 24 datasets
The idea of Domain Generalization is to learn from one or multiple training domains, to extract a domain-agnostic model which can be applied to an unseen domain
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting.
While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited.
Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available.
Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains.
In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch.