Deep Convolutional Network

Convolutional Networks is a special architecture that classifies input into local receptive fields with pooling layers inside the hidden layers. Because these hidden neurons have shared weights and biases, they better resemble feature maps. The network then compares these maps- instead of individual neurons like what vanilla feed forward architecture does- and finds indexes of similarities. Finally, the output layer returns the likeness of the input to the classifications. DCN is especially effective in image recognition where it compares images in bulk rather by pixel.

Deconvolutional Network

DN are convolutional neural networks that work in a reversed process. The goal is to restore or reconstruct data that has been degraded by a convolving method. Deconvolution network is a shape generator that produces object segmentation from the given data. Examples include up-sampling each pixel in image clarification.

Deep Convolutional Inverse Graphics Network

This network has two parts : The model learns a representation of images, disentangled with scene structure and viewing transformations such as depth rotation and lighting variations. This generative model reconstructs the input and outputs a more complete and sufficient model (in image processing, transforming original input).