Embedding Visual Hierarchy with Deep Networks for Large-Scale Visual Recognition.

In this paper, a layer-wise mixture model (LMM) is developed to support hierarchical visual recognition, where a Bayesian approach is used to automatically adapt the visual hierarchy to the progressive improvements of the deep network along the time. Our LMM algorithm can provide an end-to-end approach for jointly learning: 1) the deep network for achieving more discriminative deep representations...