A General Framework for Domain Adversarial Learning
Deep convolutional networks have provided significant advances in recent years, but progress has primarily been limited to fully supervised settings, requiring large amounts of human annotated data for training. Recent results in adversarial adaptive representation learning demonstrate that such methods can also excel when learning in sparse/weakly labeled settings across modalities and domains. In this talk, I will present a general framework for domain adversarial learning, by which a game is played between a discriminator which seeks to determine whether an image arises from the large labeled data source or from the new sparsely labeled dataset, while the representation seeks to limit the distinguishability of the two data sources. Together this game produces a model adapted for the new domain with minimal or no new human annotations.
Judy Hoffman is a Postdoctoral Researcher in the Stanford Computer Vision group, working with Fei-Fei Li. Her research focuses on developing learning representations and recognition models with limited human annotations. She received her PhD in Electrical Engineering and Computer Science from University of California, Berkeley in Summer 2016, where she was advised by Trevor Darrell and Kate Saenko. She is interested in lifelong learning, adaptive methods, and adversarial models.