Real World Domain Adaptation

Uab barcelona spain daniel cvc uab es javier marin.

Real world domain adaptation. Existing domain adaptation methods rely on rich prior knowledge about the relationship between the label sets of source and target domains which greatly limits their application in the wild. Lopez cvc and c. We show that by using synthetic data and domain adaptation we are able to reduce the number of real world samples needed to achieve a given level of performance by up to 50 times using only randomly generated simulated objects. To address this problem we have been developing domain adaptation algorithms to transfer knowledge from visual recognition systems trained on some available labeled data to the real world of natural images.

A key part of our theory is the observation that in many realistic domain adaptation scenarios we do not need such a powerful measure as variational. Almost anyone who has deployed machine learning systems in the real world has encountered the task of domain adaptation. Domain adaptation is the machine learning paradigm that aims to learn a model from a source domain that can perform well on a different but related target domain. For example large scale speech recognition systems need to work well across arbitrary speech regardless.

In this paper we summarize and compare the latest unsupervised domain adaptation methods in computer vision applications. Domain adaptation aims to transfer knowledge in the presence of the domain gap. Uab barcelona spain david vazquez cvc uab es antonio m. Such as having training labeled data from the left and trying to classify real world images on the right.

This paper intro duces universal domain adaptation uda that requires no. We build our models from some fixed source domain but we wish to deploy them across one or more different target domains. Between real valued distributions cannot be computed from finite samples 2 9 and therefore is not useful to us when investigating representations for domain adaptation on real world data. Existing depth enhancement methods use simplistic noise models and cannot generalize well under real world conditions.

Uab barcelona spain antonio cvc uab es daniel ponsa cvc and c. Domain adaptation of virtual and real worlds for human detection using active learning david vazquez cvc and c. In this paper a coupled real synthetic domain adaptation method is proposed which enables domain transfer between high quality depth simulators and real depth camera information for super resolution depth recovery. Cross domain indoor wifi localization and cross domaintext classification.