KL Divergence: Forward vs Reverse?

KL Divergence is a measure of how different two probability distributions are. It is a non-symmetric distance function, and each arrangement has its own interesting property, especially when we use it in optimization settings e.g. Variational Bayes method.

Conditional Variational Autoencoder: Intuition and Implementation

An extension to Variational Autoencoder (VAE), Conditional Variational Autoencoder (CVAE) enables us to learn a conditional distribution of our data, which makes VAE more expressive and applicable to many interesting things.

Variational Autoencoder: Intuition and Implementation

Variational Autoencoder (VAE) (Kingma et al., 2013) is a new perspective in the autoencoding business. It views Autoencoder as a bayesian inference problem: modeling the underlying probability distribution of data.

Deriving Contractive Autoencoder and Implementing it in Keras

Contractive Autoencoder is more sophisticated kind of Autoencoder compared to the last post. Here, we will dissect the loss function of Contractive Autoencoder and derive it so that we could implement it in Keras.

Many flavors of Autoencoder

Autoencoder is a family of methods that answers the problem of data reconstruction using neural net. There are several variation of Autoencoder: sparse, multilayer, and convolutional. In this post, we will look at those different kind of Autoencoders and learn how to implement them with Keras.

Level Set Method Part II: Image Segmentation

Level Set Method is an interesting classical (pre deep learning) Computer Vision method based on Partial Differential Equation (PDE) for image segmentation. In this post, we will look at its application in image segmentation.

Level Set Method Part I: Introduction

Level Set Method is an interesting classical (pre deep learning) Computer Vision method based on Partial Differential Equation (PDE) for image segmentation. In this post, we will look at the intuition behind it.

Residual Net

In this post, we will look into the record breaking convnet model of 2015: the Residual Net (ResNet).

Generative Adversarial Nets in TensorFlow

Let's try to implement Generative Adversarial Nets (GAN), first introduced by Goodfellow et al, 2014, with TensorFlow. We'll use MNIST data to train the GAN!

The Kebab Bonanza

Turkey = Kebab; Kebab = Turkey