Reducing the number of masks to accelerate the neural network visualization of RISE
Tomoki Nakada, Kousuke Imamura · 2023
RISE is one of the methods used for visualizing the basis of neural network decisions in image recognition. RISE creates a heat map showing the importance of various parts of an image by observing the response of the network while partially obscuring the input image with a random mask. However, this method requires many mask images to obtain stationarity, resulting in a huge amount of computation time. In this study, we use a non-random patch mask that passes through only one limited region in addition to an improved random mask to reduce the number of masks needed, thereby speeding up the RISE process.