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Tags: #programming
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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!
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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 ...
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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, ...
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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 ...
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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 ...
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Many flavors of Autoencoder
Autoencoder is a family of methods that answers the problem of data reconstruction using neural net. There are several variation ...
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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 ...
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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 ...
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Residual Net
In this post, we will look into the record breaking convnet model of 2015: the Residual Net (ResNet).
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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 ...