Domain Adaptation Google Scholar

M chen kq weinberger j blitzer.

Domain adaptation google scholar. Google scholar provides a simple way to broadly search for scholarly literature. Discriminatively learning domain invariant features for unsupervised domain adaptation. Advances in neural information processing systems 2456 2464 2011. The following articles are merged in scholar.

When a model learned in a domain is applied to a different domain even if in the same task there is no guarantee of accuracy. Haluaisimme näyttää tässä kuvauksen mutta avaamasi sivusto ei anna tehdä niin. Deep networks have been used to learn transferable representations for domain adaptation. Connecting the dots with landmarks.

Research scholar at indian institute of technology kanpur india cited by 84 deep learning computer vision machine learning domain adaptation adversarial learning. Co training for domain adaptation. Search across a wide variety of disciplines and sources. Elsevier biocyber biomed eng 38 3 671 683 crossref google scholar.

Their combined citations are counted only for the first article. Articles theses books abstracts and court opinions. Alirezazadeh p hejrati b monsef esfehani a fathi a 2018 representation learning based unsupervised domain adaptation for classification of breast cancer histopathology images. This is a very important issue when deep learning and machine learning are applied in the field.

Download google scholar copy bibtex abstract we propose associative domain adaptation a novel technique for end to end domain adaptation with neural networks the task of inferring class labels for an unlabeled target domain based on the statistical properties of a labeled source domain. Existing deep domain adaptation methods systematically employ popular hand crafted networks designed specifically for image classification tasks leading to sub optimal domain adaptation performance.