ATTENTION INCORPORATE NETWORK: A NETWORK CAN ADAPT VARIOUS DATA SIZE

Liangbo He, Hao Sun

Abstract: In traditional neural networks for image processing, the inputs of the neural networks should be the same size such as 224×224×3. But how can we train the neural net model with different input size? A common way to do is image deformation which accompany a problem of information loss (e.g. image crop or wrap). In this paper we propose a new network structure called Attention Incorporate Network(AIN). It solve the problem of different size of input images and extract the key features of the inputs by attention mechanism, pay different attention depends on the importance of the features not rely on the data size. Experimentally, AIN achieve a higher accuracy, better convergence comparing to the same size of other network structure.

Keywords: attention mechanism, various image size