Karthik Yearning Deep Learning

U-GAT-IT Architecture

Paper: U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation Architecture: Generator: Let x ∈ {Xs, Xt} represent a sample from the source and the target domain. Our translation model Gs→t consists of an encoder Es, a decoder Gt, and an auxiliary class... Read more

Digital Matting

Paper: A Late Fusion CNN for Digital Matting This paper studies the structure of a deep convolutional neural network to predict the foreground alpha matte by taking a single RGB image as input. Our network is fully convolutional with two decoder branches for the foreground and background classification respectively. Then a fusion branch is ... Read more

Class Activation Map

Learning Deep Features for Discriminative Localization We know that Convolution Neural Networks are good at classification tasks. This paper decodes how previous layer activation contribute for localization tasks even though the network is being trained on classification tasks. Using Global Average pooling layer, the localization ability... Read more

AI @ fb

From Satellite Imagery to Disaster Insights Large-Scale Visual Relationship Understanding Spatially Invariant Unsupervised Object Detection with Convolutional Neural Networks SGD Implicitly Regularizes Generalization Error Rethinking floating point for deep learning A2 -Nets: Double Attention Networks Explore-Exploit:... Read more

All about Seq2seq

Sequence to Sequence Learning Sequence to sequence model comprises of two sequence networks one at the encoder side and other at the decoder side. These sequence networks are RNN. The encoder networks maps the input sequence to a vector of a fixed dimension. The decoder network decodes the target sequence from the encoded vector. Seq2seq... Read more

Understanding CNN

This is a summarization of the Explanatory Graphs for CNNs paper. A graphical model is employed to understand the hierarchial feature representation hidden inside the CNN network which facilitates the classification , localization applications in Deep learning. The feature representation pattern from each filter is extracted to understand using... Read more

Gated Graph Sequence Neural Networks

Gated Graph Sequence Neural Networks Authors: Yujia Li & Richard Zemel Department of Computer Science, University of Toronto Toronto, Canada {yujiali,zemel}@cs.toronto.edu Marc Brockschmidt & Daniel Tarlow Microsoft Research Cambridge, UK {mabrocks,dtarlow}@microsoft.com ... Read more

Multi Target Prediction

In this blog, lets talk about multi target variables. In a generic machine learning model prediction scenario, where we will have input features and single target variable. Input Feature Output Feature x1 , x2 , x3 , x4… y Assigning multiple target variables would throw an error on line... Read more

Neural Network without Math

Let’s cover the fundamentals. What is Convolution? Convolution uses kernels/filter to extract information from images. Different kernels can extract different representations from images. For different tasks such as sharpening , edge detection , blurring different kernels can be employed. Kernel is matrix which slides upon the image to extrac... Read more