ACGAN Architectural Design

ACGAN Model

Conditional Image Synthesis with Auxiliary Classifier GANs paper

  • CNN have brought advances in image classification

  • CNN can also be reversed to generate images from scratch (generative models)

  • One type of generative model are generative adversarial networks (GANs)

  • Special type of GAN is Auxiliary Classifier GANs (ACGAN)

  • GAN with class-label conditioning to generate images

Example of Generated Images Conditioned on Label

Introduction to Generative Adversarial Networks (GANs)

  • GAN is composed of two competing neural network models (often CNNs)

  • Generator: takes noise input and generate a realistic image

  • Discriminator: takes real and fake images and has to distinguish the fake from the real

  • Two networks play an adversarial game

  • generator learns to produce more and more realistic samples

  • discriminator learns to get better and better at distinguishing generated data from real data.

  • networks are trained simultaneously

Training on real image:

  • Discriminator should classify real image as real

  • Ouput probability close to 1

Training on fake image:

  • Generator generate fake image from noise

  • Discriminator should classify fake image as fake

  • Ouput probability close to 0

Auxiliary Classifier ACGAN

Proposed a new method for improved training of GANs by conditioning input with class labels.

Multi-input multi-output network:

  • Inputs: class embedding and noise vector

  • Outputs: binary classifier (fake/real images) and multi-class classifier (image classes)

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Implementation of ACGAN model

  • Generator

  • Discriminator

  • dual loss functions