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.