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Evaluation of Deep Neural Network Domain Adaptation Techniques for Image Recognition

  • It has been well proved that deep networks are efficient at extracting features from a given (source) labeled dataset. However, it is not always the case that they can generalize well to other (target) datasets which very often have a different underlying distribution. In this report, we evaluate four different domain adaptation techniques for image classification tasks: DeepCORAL, DeepDomainConfusion, CDAN and CDAN+E. These techniques are unsupervised given that the target dataset dopes not carry any labels during training phase. We evaluate model performance on the office-31 dataset. A link to the github repository of this report can be found here: https://github.com/agrija9/Deep-Unsupervised-Domain-Adaptation.

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Document Type:Preprint
Author:Alan Preciado-Grijalva, Venkata Santosh Sai Ramireddy Muthireddy
Number of pages:7
ArXiv Id:http://arxiv.org/abs/2109.13420
Date of first publication:2021/09/28
Keyword:convolutional neural networks; domain adaptation; transfer learning; unsupervised learning
Departments, institutes and facilities:Fachbereich Informatik
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2021/10/04