Let us hear what he has to say about his defense and an abstract of his thesis.

Deep learning has brought impressive improvements in many fields, thanks to end-to-end data-driven optimization. However, people have little control over the system during training and limited understanding about the structure of knowledge being learned. In this thesis, we study theory and applications of adversarial and structured knowledge learning: 1) learning adversarial knowledge with human interaction or by incorporating human-in-the-loop; 2) learning structured knowledge by modelling contexts and users’ preferences via distance metric learning.

In the first part, we teach a robotic arm to learn robust manipulation grasps that can withstand perturbations, through end-to-end optimization with a human adversary. Specifically, we formulate the problem as a two-player game with incomplete information, played by a human and a robot, where the human’s goal is to minimize the reward the robot can get. We then extend this idea to improve the sample efficiency of deep reinforcement learning by incorporating human in the training loop. We presented a portable, interactive and parallel platform for human-agent curriculum learning experience.

In the second part, we present two works that address different aspects of structured representation learning. First, we proposed a self-training framework to improve distance metric learning. The challenge is the noise in pseudo labels, which prevents exploiting additional unlabeled data. Therefore, we introduced a new feature basis learning component for the teacher-student network, which better measures pairwise similarity and selects high confidence pairs. Second, we address image-attribute query, which allows a user to customize image-to-image retrieval by designating desired attributes in target images. We achieve this by adopting a composition module, which enforces object-attribute factorization and an attribute-set synthesis module to deal with sample insufficiency.

Looking back, Prof. Kuo is the person I want to thank the most for making my PhD dream a reality and I look forward to maintaining close connections with all the lab members in the future!