deep learning for computer vision there are few things to note herethe first layer acts as collection of various edge detectors at that stagethe activations retain almost all of the information present in the initial picture as you go higherthe activations become increasingly abstract and less visually interpretable they begin to encode higher-level concepts such as "cat earand "cat eye higher presentations carry increasingly less information about the visual contents of the imageand increasingly more information related to the class of the image the sparsity of the activations increases with the depth of the layerin the first layerall filters are activated by the input imagebut in the following layersmore and more filters are blank this means the pattern encoded by the filter isn' found in the input image we have just evidenced an important universal characteristic of the representations learned by deep neural networksthe features extracted by layer become increasingly abstract with the depth of the layer the activations of higher layers carry less and less information about the specific input being seenand more and more information about the target (in this casethe class of the imagecat or doga deep neural network effectively acts as an information distillation pipelinewith raw data going in (in this casergb picturesand being repeatedly transformed so that irrelevant information is filtered out (for examplethe specific visual appearance of the image)and useful information is magnified and refined (for examplethe class of the imagethis is analogous to the way humans and animals perceive the worldafter observing scene for few secondsa human can remember which abstract objects were present in it (bicycletreebut can' remember the specific appearance of these objects in factif you tried to draw generic bicycle from memorychances are you couldn' get it even remotely righteven though you've seen thousands of bicycles in your lifetime (seefor examplefigure try it right nowthis effect is absolutely real you brain has learned to completely abstract its visual input--to transform it into high-level visual concepts while filtering out irrelevant visual details--making it tremendously difficult to remember how things around you look figure left at empt to draw bicycle from memory rightwhat schematic bicycle should look like licensed to