AI summary

This paper introduces a recurrent neural network (RNN) model, the Recurrent Attention Model (RAM), that uses reinforcement learning to extract information from images by adaptively selecting a sequence of regions, outperforming a convolutional neural network baseline on cluttered image classification tasks and learning to track a simple object on a dynamic visual control problem.

Abstract

Applying convolutional neural networks to large images is computationally ex-pensive because the amount of computation scales linearly with the number of image pixels. We present a novel recurrent neural network model that is ca-pable of extracting information from an image or video by adaptively selecting a sequence of regions or locations and only processing the selected regions at high resolution. Like convolutional neural networks, the proposed model has a degree of translation invariance built-in, but the amount of computation it per-forms can be controlled independently of the input image size. While the model is non-differentiable, it can be trained using reinforcement learning methods to learn task-specific policies. We evaluate our model on several image classification tasks, where it significantly outperforms a convolutional neural network baseline on cluttered images, and on a dynamic visual control problem, where it learns to track a simple object without an explicit training signal for doing so.