moonhwan JeongTensorflow2.0 PGGAN: Progressive Growing of GANs for Improved Quality, Stability, and VariationHere, I introduce a simple code to implement PGGAN in Tensorflow 2.0.Jan 13, 20211Jan 13, 20211
moonhwan JeongUpdating old tensorflow codes to new tensorflow 2.0+ style.In this post I will introduce an example of how to update legacy tensorflow codes to new style. tensorflow 2.0 recommends using Keras…Aug 8, 2020Aug 8, 2020
moonhwan Jeong3D Face Reconstruction: Make a Realistic Avatar from a PhotoIn this post, I’ll introduce techniques for reconstructing a 3D face from a Photo. Creating a unreal character from a picture is familiar…Dec 20, 2019Dec 20, 2019
moonhwan JeongTensorflow-BEGAN: Boundary Equilibrium Generative Adversarial NetworksI’ve covered GAN and DCGAN in past posts. In 2017, Google published a great paper. The title of paper is “BEGAN: Boundary Equilibrium…Apr 16, 20191Apr 16, 20191
moonhwan JeongMake dataset from images in TensorflowA data-set is needed to train the model. In the last article, we covered the model for generating faces. I used Celeb_A dataset(link)…Mar 14, 2019Mar 14, 2019
moonhwan JeongDCGAN-Tensorflow: For more stable trainingSince Ian Goodfellow’s paper, GAN has been applied to many fields, but its instability has always caused problems. The GAN has to solve…Mar 8, 20191Mar 8, 20191
moonhwan JeongGAN with Tensorflow: Basics of Generative Adversarial NetworksMachine learning is generally classified into three types: Supervised learning, Unsupervised learning and Reinforcement learning.Feb 21, 2019Feb 21, 2019
