Comparison of Neuronal Attention Models
This addresses efficiency issues in image processing for researchers and practitioners, but it appears incremental as it builds on existing attention models.
The paper tackles the problem of time-consuming pixel-per-pixel analysis in convolutional neural networks for large images by proposing a Neuronal Attention Model (NAM) to focus on small regions, resulting in improved training time and accuracy with a size-independent method.
Recent models for image processing are using the Convolutional neural network (CNN) which requires a pixel per pixel analysis of the input image. This method works well. However, it is time-consuming if we have large images. To increase the performance, by improving the training time or the accuracy, we need a size-independent method. As a solution, we can add a Neuronal Attention model (NAM). The power of this new approach is that it can efficiently choose several small regions from the initial image to focus on. The purpose of this paper is to explain and also test each of the NAM's parameters.