figure deep learning for computer vision filter pat erns for layer block _conv these filter visualizations tell you lot about how convnet layers see the worldeach layer in convnet learns collection of filters such that their inputs can be expressed as combination of the filters this is similar to how the fourier transform decomposes signals onto bank of cosine functions the filters in these convnet filter banks get increasingly complex and refined as you go higher in the modelthe filters from the first layer in the model (block _conv encode simple directional edges and colors (or colored edgesin some casesthe filters from block _conv encode simple textures made from combinations of edges and colors the filters in higher layers begin to resemble textures found in natural imagesfeatherseyesleavesand so on visualizing heatmaps of class activation 'll introduce one more visualization techniqueone that is useful for understanding which parts of given image led convnet to its final classification decision this is helpful for debugging the decision process of convnetparticularly in the case of classification mistake it also allows you to locate specific objects in an image this general category of techniques is called class activation map (camvisualizationand it consists of producing heatmaps of class activation over input images class activation heatmap is grid of scores associated with specific output classcomputed for every location in any input imageindicating how important each location is with licensed to