Deep Learning @ UGA

Visual Analytics in Deep Learning: Highlights from An Interrogative Survey

Deep learning has recently seen rapid development and received significant attention due to its state-of-the-art performance on previously-thought hard problems. However, because of the internal complexity and nonlinear structure of deep neural networks, the underlying decision making processes for why these models are achieving such performance are challenging and sometimes mystifying to interpret. As deep learning spreads across domains, it is of paramount importance that we equip users of deep learning with tools for understanding when a model works correctly, when it fails, and ultimately how to improve its performance. Standardized toolkits for building neural networks have helped democratize deep learning; visual analytics systems have now been developed to support model explanation, interpretation, debugging, and improvement. We present a survey of the role of visual analytics in deep learning research, which highlights its short yet impactful history and thoroughly summarizes the state-of-the-art using a human-centered interrogative framework, focusing on the Five W's and How (Why, Who, What, How, When, and Where). We conclude by highlighting research directions and open research problems. This survey helps researchers and practitioners in both visual analytics and deep learning to quickly learn key aspects of this young and rapidly growing body of research, whose impact spans a diverse range of domains.

Fred Hohman is a PhD student at Georgia Tech's College of Computing. His research combines principles from human-computer interaction and techniques from machine learning to improve deep learning interpretability using interactive data visualization. He won the 2018 NASA Space Technology Research Fellowship and Microsoft AI for Earth Award for using AI to improve sustainability. He received his B.S. in mathematics and physics from the University of Georgia, and has conducted research at Microsoft Research, NASA Jet Propulsion Laboratory, and Pacific Northwest National Laboratory.