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Advanced GAN Techniques For Semi-Supervised Learning And Realistic Image Generation.pdf

1606.03498.pdf
Preview of Advanced GAN Techniques for Semi-Supervised Learning and Realistic Image Generation
🔗 Source: arxiv.org
📊 Size: 2.24 MB
📄 Pages: 10 pages
⬇️ Downloads: 196

Summary

The authors address the challenges of GAN instability and non-convergence by introducing several innovative methods.

Key Techniques:

1. Feature Matching: Instead of maximizing discriminator output, the generator is trained to match the expected values of intermediate layer features in the discriminator, encouraging the generation of data that aligns with real data statistics.

2. Minibatch Discrimination: This technique prevents the generator from collapsing to a single mode by allowing the discriminator to consider multiple examples simultaneously, coordinating its gradients to increase dissimilarity among generated samples.

3. Virtual Batch Normalization: An extension of batch normalization, helping stabilize training by normalizing features across mini-batches.

Achievements:

- State-of-the-art results in semi-supervised classification on MNIST, CIFAR-10, and SVHN.
- Generation of high-quality images with human error rates of 21.3% on CIFAR-10.
- Successful learning of recognizable features from ImageNet classes.

Future Prospects:

The authors hope these techniques will provide a foundation for future work, potentially offering formal guarantees of convergence in GAN training.

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Description

The techniques enable effective classification on MNIST, CIFAR-10, and SVHN datasets, as well as visually compelling image synthesis.

Technical Information

  • File Format: PDF
  • File Size: 2.24 MB
  • Pages: 10
  • Language: EN
  • Total Downloads: 196
  • Last Updated: 2 hours ago

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