Inceptionism : Going Deeper into Neural Networks
►http://googleresearch.blogspot.co.uk/2015/06/inceptionism-going-deeper-into-neural.html
Faut absolument que j’arrive à essayer ^^ (...)
One way to visualize what goes on is to turn the network upside down and ask it to enhance an input image in such a way as to elicit a particular interpretation. Say you want to know what sort of image would result in “Banana.” Start with an image full of random noise, then gradually tweak the image towards what the neural net considers a banana (see related work in [1], [2], [3], [4]). By itself, that doesn’t work very well, but it does if we impose a prior constraint that the image should have similar statistics to natural images, such as neighboring pixels needing to be correlated.
Instead of exactly prescribing which feature we want the network to amplify, we can also let the network make that decision. In this case we simply feed the network an arbitrary image or photo and let the network analyze the picture. We then pick a layer and ask the network to enhance whatever it detected. Each layer of the network deals with features at a different level of abstraction, so the complexity of features we generate depends on which layer we choose to enhance. For example, lower layers tend to produce strokes or simple ornament-like patterns, because those layers are sensitive to basic features such as edges and their orientations.
http://3.bp.blogspot.com/-4Uj3hPFupok/VYIT6s_c9OI/AAAAAAAAAlc/_yGdbbsmGiw/s1600/ibis.png 
http://3.bp.blogspot.com/-R15_fyB-ZpE/VYIV-Uu9iwI/AAAAAAAAAl4/o3heQNGpVRU/s640/Funny-Animals.png 
http://1.bp.blogspot.com/-XZ0i0zXOhQk/VYIXdyIL9kI/AAAAAAAAAmQ/UbA6j41w28o/s640/building-dreams.png #images #réseaux_de_neurones #machine_learning #psychédélique
