Domain Adaptation networks for noisy image classification

Domain Adaptation networks for noisy image classification

Zhang, Chengqiu (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Intelligent Systems)

van Gemert, Jan (mentor)
Pintea, Silvia (mentor)

Delft University of Technology

2017-08-21

In this thesis, we propose a novel unsupervised clean-noisy datasets adaptation algorithm based on standard deep learning networks. Specifically, we jointly learn a shared feature encoder for two tasks: 1)supervised classification trained on labeled source (clean) dataset, and 2) unsupervised adaptation to map discriminant features from both source and target domains to a common space. Our proposed network is optimized by a step backpropagation similarly as some of the Generative Adversarial Networks (GANs).

We evaluate our proposed network on two datasets, where a improvement of classification performance is achieved (up to ~19% in average accuracy over all noise levels) over state-of-the-art denoising algorithm BM3D. Interestingly, we also observe that our proposed approach improves the feature transferability on deep networks with its task-specific learning steps.

http://resolver.tudelft.nl/uuid:a62dba04-7e82-4f69-a977-f65563636158

Student theses

master thesis

© 2017 Chengqiu Zhang