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Conditional Generative Adversarial Nets in TensorFlow
Having seen GAN, VAE, and CVAE model, it is only proper to study the Conditional GAN model next!

KL Divergence: Forward vs Reverse?
KL Divergence is a measure of how different two probability distributions are. It is a nonsymmetric distance function, and each ...

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, ...

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 ...

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 ...

Many flavors of Autoencoder
Autoencoder is a family of methods that answers the problem of data reconstruction using neural net. There are several variation ...

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 ...

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 ...

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 ...