  The inference structures and computational complexity of existing deep neural
networks, once trained, are fixed and remain the same for all test images.
However, in practice, it is highly desirable to establish a progressive
structure for deep neural networks which is able to adapt its inference process
and complexity for images with different visual recognition complexity. In this
work, we develop a multi-stage progressive structure with integrated confidence
analysis and decision policy learning for deep neural networks. This new
framework consists of a set of network units to be activated in a sequential
manner with progressively increased complexity and visual recognition power.
Our extensive experimental results on the CIFAR-10 and ImageNet datasets
demonstrate that the proposed progressive deep neural network is able to obtain
more than 10 fold complexity scalability while achieving the state-of-the-art
performance using a single network model satisfying different
complexity-accuracy requirements.
