Feature Activation Regression for Image-to-Image Feature Generation

With the need for explainable AI, several visualization methods have been developed to explore neural networks. Our proposed work seeks to isolate and generate image features of input data using any neural network. We employed a modified gradient ascent algorithm with a smoothed gradient loss function for image-to-image feature generation.