@unpublished{Preciado-GrijalvaMuthireddy2021,
  author    = {Preciado-Grijalva, Alan and Muthireddy, Venkata Santosh Sai Ramireddy},
  title     = {Evaluation of Deep Neural Network Domain Adaptation Techniques for Image Recognition},
  doi       = {10.48550/arXiv.2109.13420},
  institution = {Fachbereich Informatik},
  pages     = {7},
  year      = {2021},
  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: https://github.com/agrija9/Deep-Unsupervised-Domain-Adaptation.},
  language  = {en}
}