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CS231n Convolutional Neural Networks for Visual Recognition
Multiclass SVM optimization demo
Image Kernels explained visually
FC:
ConvNetJS demo: Classify toy 2D data
CNN:
Using this pretrained model, we will compute class saliency maps as described in Section 3.1 of [2].
A saliency map tells us the degree to which each pixel in the image affects the classification score for that image. To compute it, we compute the gradient of the unnormalized score corresponding to the correct class (which is a scalar) with respect to the pixels of the image. If the image has shape (3, H, W) then this gradient will also have shape (3, H, W); for each pixel in the image, this gradient tells us the amount by which the classification score will change if the pixel changes by a small amount. To compute the saliency map, we take the absolute value of this gradient, then take the maximum value over the 3 input channels; the final saliency map thus has shape (H, W) and all entries are nonnegative.
[2] Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. "Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps", ICLR Workshop 2014.
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps