Finding Archetypal Spaces Using Neural Networks Archetypal analysis is a data decomposition method that describes each observation in a dataset as a convex combination of pure types or archetypes. These archetypes represent extrema of a data space in which there is a tradeoff between features, such as in biology where different combinations of traits provide optimal fitness for different environments. Existing methods for archetypal analysis work well when a linear relationship exists between the feature space and the archetypal space. However, such methods are not applicable to systems where the feature space is generated nonlinearly from the combination of archetypes, such as in biological systems or image transformations. Here, we propose a reformulation of the problem such that the goal is to learn a nonlinear transformation of the data into a latent archetypal space. To solve this problem, we introduce Archetypal Analysis network (AAnet), which is a deep neural network framework for learning and generating from a latent archetypal representation of data. We demonstrate stateoftheart recovery of groundtruth archetypes in nonlinear data domains, show AAnet can generate from data geometry rather than from data density, and use AAnet to identify biologically meaningful archetypes in singlecell gene expression data.

GRNet: An Efficient Group Ranking Network for Face Attribute Recognition Face attribute recognition methods have been far from realworld applications despite remarkable progress in recent years. Most of these methods fail to mine attribute relationships with traditional cross entropy loss and hold considerable computational complexity. In this work, we propose a group ranking network (GRNet) to investigate substantial relationships among face attributes. First, an attribute grouping manner is designed to capture interactions among spatially related attributes and enhance computational efficiency. Second, a new supervision signal is presented to model attribute ranking relationships. Under the supervision of ranking loss, GRNet learns intraattribute and interattribute ranking features to intensify the representational capability of attribute models. We evaluate our approach on aligned and unaligned CelebA datasets. Results show that the performance of the proposed approach is superior to other startoftheart methods on attribute recognition.