We develop a framework for learning generic, expressive image priors that capture the statistics of natural scenes and can be used for a variety of machine vision tasks. The appro...
Scientists find that the human perception is based on the similarity on the manifold of data set. Isometric feature mapping (Isomap) is one of the representative techniques of man...
Jie Chen, Ruiping Wang, Shiguang Shan, Wen Gao, Xi...
We describe a directed bilinear model that learns higherorder groupings among features of natural images. The model represents images in terms of two sets of latent variables: one...
Jack Culpepper, Jascha Sohl-Dickstein, Bruno Olaha...
Recognizing 3D objects from arbitrary view points is one of the most fundamental problems in computer vision. A major challenge lies in the transition between the 3D geometry of o...
We study a sparse coding learning algorithm that allows for a simultaneous learning of the data sparseness and the basis functions. The algorithm is derived based on a generative m...