PyTorch DCGAN From Scratch - Improving The Architecture with CNNs

PyTorch DCGAN From Scratch -  Improving The Architecture with CNNs

We build a generative adversarial network based on convolutional neural networks and train it on the CelebA dataset. This is a huge improvement from the previous simple fully connected GAN implemented in previous videos.

In this video we build a generative adversarial network based on convolutional neural networks and train it on the CelebA dataset. This is a huge improvement from the previous simple fully connected GAN implemented in previous videos.

DCGAN paper: https://arxiv.org/abs/1511.06434

CelebA dataset used in video: https://www.kaggle.com/dataset/504743cb487a5aed565ce14238c6343b7d650ffd28c071f03f2fd9b25819e6c9

Github Repository: https://github.com/aladdinpersson/Machine-Learning-Collection

OUTLINE:

  • 0:00 - Introduction
  • 0:26 - Quick Paper Recap
  • 4:31 - Implementation of Discriminator
  • 9:38 - Implementation of Generator
  • 15:27 - Weight initialization and test model
  • 19:09 - Setup of training
  • 31:36 - Training on MNIST
  • 32:20 - Modifications to CelebA dataset
  • 33:52 - Training on CelebA and ending

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