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Deep Learning · GANs · PyTorch · Generative AI
PyTorch, Deep Learning, Generative Models LinksCode All projects
Overview
Implementation of a Deep Convolutional Generative Adversarial Network (DCGAN) using PyTorch to generate realistic handwritten digits. The model learns to produce images indistinguishable from real MNIST samples.
Architecture
Generator Network
- Takes random noise vector as input
- Upsamples through transposed convolutions
- Outputs 28x28 grayscale images
- Uses BatchNorm and ReLU activations
Discriminator Network
- Takes 28x28 images as input
- Downsamples through convolutions
- Outputs probability of image being real
- Uses LeakyReLU and Dropout
Training Process
The adversarial training involves:
- Discriminator Training
- Learn to distinguish real MNIST images from generated fakes
- Maximize classification accuracy
- Generator Training
- Learn to produce images that fool the discriminator
- Minimize discriminator’s ability to detect fakes
Results
The trained generator produces realistic handwritten digits across all classes (0-9), demonstrating:
- Diverse digit styles
- Clear, legible outputs
- Smooth interpolation in latent space
Key Learnings
- GAN training dynamics and stability
- Importance of architectural choices in DCGANs
- Hyperparameter tuning for convergence