ZF Net - rugbyprof/5443-Data-Mining GitHub Wiki

ZFNET(Visualizing and Understanding Convolutional Networks)

Convolutional Neural Networks:

In machine learning, a convolutional neural network (CNN, or Conv Net) is a class of deep, feed-forward artificial neural networks that has successfully been applied to analyzing visual imagery. CNNs use a variation of multilayer perceptron’s designed to require minimal preprocessing. Reference: https://en.wikipedia.org/wiki/Convolutional_neural_network

Understanding the operation of a convnet requires interpreting the feature activity in intermediate layers. We present a novel way to map these activities back to the input pixel space, showing what input pattern originally caused a given activation in the feature maps. We perform this mapping with a Deconvolutional Network (deconvnet) (Zeiler et al., 2011). A deconvnet can be thought of as a convnet model that uses the same components (filtering, pooling) but in reverse, so instead of mapping pixels to features does the opposite.In (Zeiler et al., 2011), deconvnets were proposed as a way of performing unsupervised learning. Here,they are not used in any learning capacity, just as a probe of an already trained convnet.