Visualizing-and-Understanding-Convolutional-neural-networks

This is a straight from scratch implementation of paper "Visualizing and Understanding Convolutional neural networks"-Matthew D. Zeiler and Rob Fergus (https://cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf)

This implementation is using keras with tensorflow backend.

The implementation is mainly divided into two parts-

1. Feature visualization with Deconvnet.
2. Occlusion Sensitivity.

Part 1- Feature visualization with Deconvnet This is described in notebook "Feature visualization using Deconvnets in Keras". Here the term deconvolution refers to transpose convolution.

Original image -

Features detected in feature map 127 of convolutional layer 3 of block 3 of VGG16 model-

Part 2- Occlusion sensitivity This experiment is carried out to test if the convnet is actualy locating and detecting the desired object in image and not some pattern or object in background.

The experiment is described in notebook- "Occlusion experiment".The experiment is carried out on MNIST hanwritten digit data set.

Original image -

Occlusion map -