Recent years have witnessed the great success of convolutional neural network (CNN) based models in the field of computer vision. CNN is able to learn hierarchically abstracted features from images in an end-to-end training manner. However, most of the existing CNN models only learn features through a feedforward structure and no feedback information from top to bottom layers is exploited to enable the networks to refine themselves. In this paper, we propose a "Learning with Rethinking" algorithm. By adding a feedback layer and producing the emphasis vector, the model is able to recurrently boost the performance based on previous prediction. Particularly, it can be employed to boost any pre-trained models. This algorithm is tested on four object classification benchmark datasets: CIFAR-100, CIFAR-10, MNIST-background-image and ILSVRC-2012 dataset. These results have demonstrated the advantage of training CNN models with the proposed feedback mechanism.
The most related work on utilizing the recurrent neural network for object recognition would be dasNet @cite_53 . It makes use of a reinforcement learning strategy to iteratively adjust some weights of feature maps. And final classification results are made after several iterations. Our Learning with Rethinking" algorithm differs from dasNet in three major aspects. Firstly, we use a neural network to feedback information into lower layers, which is relatively easy to calculate. Secondly, we only use the posterior probability of previous feedforward pass as the feedback information, which is much more timely and spatially efficient. Thirdly, our algorithm can be regarded as a new further training algorithm which is easy to be applied to any pre-trained models, and will further boost the performance. Comparatively, dasNet needs to train from random initialization.
@cite_53
2172010943
Traditional convolutional neural networks (CNN) are stationary and feedforward. They neither change their parameters during evaluation nor use feedback from higher to lower layers. Real brains, however, do. So does our Deep Attention Selective Network (dasNet) architecture. DasNets feedback structure can dynamically alter its convolutional filter sensitivities during classification. It harnesses the power of sequential processing to improve classification performance, by allowing the network to iteratively focus its internal attention on some of its convolutional filters. Feedback is trained through direct policy search in a huge million-dimensional parameter space, through scalable natural evolution strategies (SNES). On the CIFAR-10 and CIFAR-100 datasets, dasNet outperforms the previous state-of-the-art model.