We propose to address the open set domain adaptation problem by aligning images at both the pixel space and the feature space. Our approach, called Open Set Translation and Adaptation Network (OSTAN), consists of two main components: translation and adaptation. The translation is a cycle-consistent generative adversarial network, which translates any source image to the “style” of a target domain to eliminate domain discrepancy in the pixel space. The adaptation is an instance-weighted adversarial network, which projects both (labeled) translated source images and (unlabeled) target images into a domain-invariant feature space to learn a prior probability for each target image. The learned probability is applied as a weight to the unknown classifier to facilitate the identification of the unknown class. The proposed OSTAN model significantly outperforms the state-of-the-art open set domain adaptation methods on multiple public datasets. Our experiments also demonstrate that both the image-to-image translation and the instance-weighting framework can further improve the decision boundaries for both known and unknown classes.

We consider the scattering of light in participating media composed of sparsely and randomly distributed discrete particles. The particle size is expected to range from the scale of the wavelength to several orders of magnitude greater, resulting in an appearance with distinct graininess as opposed to the smooth appearance of continuous media. One fundamental issue in the physically-based synthesis of such appearance is to determine the necessary optical properties in every local region. Since these properties vary spatially, we resort to geometrical optics approximation (GOA), a highly efficient alternative to rigorous Lorenz-Mie theory, to quantitatively represent the scattering of a single particle. This enables us to quickly compute bulk optical properties for any particle size distribution. We then use a practical Monte Carlo rendering solution to solve energy transfer in the discrete participating media. Our proposed framework is the first to simulate a wide range of discrete participating media with different levels of graininess, converging to the continuous media case as the particle concentration increases.