Machine Learning

Introduction This blog is dedicated to the project for the course on Deep Representation Learning ( IFT6266 ) taught by Aaron Courville at Université de Montréal. Given images of size 64x64 , we mask the central region of size 32x32 . The goal of this project is to generate that missing central region conditioned on the outer border of the image and a caption describing the content of the image. Proposed Architecture We implemented a feed-forward auto-encoder architecture with  Tensorflow , which is shown in figure 1 . figure 1:  Architecture of our auto-encoder Implementation details The encoder uses a sequence of 4 down sampling 5x5 convolutions with strides of 2x2 to extract a 4x4x256 representation of the input image, while the decoder uses a sequence of 4 up sampling 5x5 convolutions of strides 2x2 to reconstruct the masked region. Each convolution is followed by a batch normalisation before the activation function. Inspired by [1] , we used a cha