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Evaluation of Deep Neural Network Domain Adaptation Techniques for Image Recognition

2021-09-28 01:22:12
Alan Preciado-Grijalva, Venkata Santosh Sai Ramireddy Muthireddy
arXiv_AI
         
Abstract

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: this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2109.13420

PDF

https://arxiv.org/pdf/2109.13420.pdf

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