Progressive Neural Networks for Image Classification
This work addresses the need for adaptive and efficient neural networks in image classification, offering a novel solution for varying complexity-accuracy trade-offs.
The paper tackles the problem of fixed inference complexity in deep neural networks by proposing a progressive structure that adapts computational complexity based on image recognition difficulty, achieving over 10-fold complexity scalability while maintaining state-of-the-art performance on CIFAR-10 and ImageNet.
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.