Talk by Kihyuk Sohn

You are all cordially invited to the AMLab seminar on Thursday October 25 at 16:00 in C3.163 (FNWI, Amsterdam Science Park), where Kihyuk Sohn (NEC) will give a talk titled “Deep Domain Adaptation in the Wild”. Afterwards there are the usual drinks and snacks.

Unsupervised domain adaptation is a promising avenue to enhance the performance of deep neural networks on a target domain, using labels only from a source domain. However, it is not well studied at which levels of representations the adaptation should happen and their complementary properties. Furthermore, the theory of domain adaptation is limited to classification problems whose source and target domains share the same task. In this talk, we address these challenges in deep domain adaptation. Firstly, we argue that the adaptation may happen at various levels of representations, such as input pixels, intermediate features, or output labels, with an injection of different insights at different levels. Secondly, we generalize the theory of domain adaptation to the case where source and target domains do not necessarily share the same classification task. We demonstrate the effectiveness of the proposed methods in several vision applications, namely, car recognition in the surveillance domain, face recognition across various ethnicity, and semantic segmentation.

Kihyuk Sohn is a researcher in the Media Analytics group of NEC Laboratories America. His research interest lies in machine learning and computer vision, with a focus on deep representation learning from large-scale, structured and multimodal data for robust visual perception. He obtained his Ph.D (2015) from the department of Electrical Engineering and Computer Science, University of Michigan.