Weight Swapping

Weight Swapping: A new method for Supervised Domain Adaptation in Computer Vision using Discrete Optimization

Datta, Leonid (TU Delft Electrical Engineering, Mathematics and Computer Science)

van Gemert, J.C. (mentor)
Bruintjes, R. (graduation committee)

Delft University of Technology

2020-08-19

Training Convolutional Neural Network (CNN) models is difficult when there is a lack of labeled training data and no unlabeled data is available. A popular method for this is domain adaptation where the weights of a pre-trained CNN model are transferred to the problem setup. The model is pre-trained on the same task but in a different domain that has plenty of labeled data samples available. In a CNN model, we can rearrange the weights of a convolutional layer by permuting them along the input channel dimension. This work shows that certain weights that are learned in the pre-trained model work well in the problem setup when the weights are rearranged in this manner. Computing the set of all possible rearrangements of the weights is computationally intractable. This work proposes two algorithms to find a good rearrangement of the weights in reasonable computation time. The solutions from the algorithms perform equally well or better than fine-tuning in the domain adaptation between SVHN and MNIST data.

deep learning
computer vision
domain adaptation
Convolutional Neural Networks (CNNs)
transfer learning

http://resolver.tudelft.nl/uuid:63511523-64d1-4b5e-ab66-26a5d0d61bd4

Student theses

master thesis

© 2020 Leonid Datta