3 accepted papers
For unsupervised domain adaptation, the target domain error can be provably reduced by having a shared input representation that makes the source and target domains indistinguishable from each other. Very recently it has been shown that it is not only critical to match the marginal input distributio…
This paper introduces a probabilistic latent variable model to address unsupervised domain adaptation problems. This is achieved by learning projections from each domain to a latent space along the classifier in the latent space to simultaneously minimizing a notion of domain disparity while maximiz…
Subspace clustering is a common modeling paradigm used to identify constituent modes of variation in data with locally linear structure. These structures are common to many problems in computer vision, including modeling time series of complex human motion. However classical subspace clustering alg…