Demystifying Variational Autoencoders: Understanding the Inner Workings of VAEs
Introduction
Variational Autoencoders (VAEs) represent a powerful class of generative models that combine neural networks with probabilistic inference.
What are Autoencoders?
Traditional autoencoders learn compressed representations through:
- Encoder: Compresses input to latent representation
- Decoder: Reconstructs input from latent representation
The Variational Approach
VAEs extend autoencoders by:
- Learning a probability distribution in latent space
- Enabling smooth interpolation between data points
- Generating new, realistic samples
Architecture Deep Dive
Encoder Network
Maps input to distribution parameters (mean and variance).
Reparameterization Trick
Enables backpropagation through sampling operations.
Decoder Network
Generates output from sampled latent vectors.
The Loss Function
VAE training optimizes:
- Reconstruction Loss: How well outputs match inputs
- KL Divergence: Regularizes latent space distribution
Applications
- Image generation
- Data augmentation
- Anomaly detection
- Drug discovery
- Music generation
VAEs vs. GANs
| Aspect | VAE | GAN |
|---|---|---|
| Training | Stable | Can be unstable |
| Output Quality | Smooth, sometimes blurry | Sharp, realistic |
| Latent Space | Structured | Less structured |
Conclusion
VAEs offer a principled approach to generative modeling with well-defined latent spaces and stable training dynamics.
Explore more deep learning concepts.