TY  - INPR
U1  - Preprint
A1  - Preciado-Grijalva, Alan
A1  - Muthireddy, Venkata Santosh Sai Ramireddy
T1  - Evaluation of Deep Neural Network Domain Adaptation Techniques for Image Recognition
N2  - 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.
KW  - transfer learning
KW  - convolutional neural networks
KW  - domain adaptation
KW  - unsupervised learning
Y1  - 2021
U6  - https://doi.org/10.48550/arXiv.2109.13420
DO  - https://doi.org/10.48550/arXiv.2109.13420
AX  - 2109.13420
SP  - 7
S1  - 7
PB  - arXiv
ER  -