🎨 Autoencoders & GANs
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# 🎨 Autoencoders & GANs ## 1. 🎯 Learning Objectives - Explain autoencoder architecture: encoder → bottleneck → decoder - Distinguish VAE (probabilistic) from standard autoencoder - Describe GAN: generator vs discriminator, adversarial training ## 2.
🎨 Autoencoders & GANs
1. 🎯 Learning Objectives
- Explain autoencoder architecture: encoder → bottleneck → decoder
- Distinguish VAE (probabilistic) from standard autoencoder
- Describe GAN: generator vs discriminator, adversarial training
2. 📖 Core Content
3.1 Autoencoder
Encoder: — compresses input to latent code. Decoder: — reconstructs input from code. Loss: — reconstruction error.
3.2 Variational Autoencoder (VAE)
VAE learns a probabilistic latent space: encoder outputs mean μ and variance σ². Latent variable z ~ N(μ, σ²).
Loss:
The KL term encourages latent space to be continuous and well-structured.
3.3 GAN (Generative Adversarial Network)
Generator G: Creates fake data from random noise z. Discriminator D: Distinguishes real from fake data.
Minimax game:
Training: Alternate between:
- Train D to distinguish real from fake
- Train G to fool D
4. 📝 Practice Questions
Q1: What is the purpose of the KL divergence term in VAE loss?Answer: It regularizes the latent space to follow a standard normal distribution, ensuring continuity (similar inputs map to nearby latent points) and completeness (every point in latent space produces valid outputs). Join Discord PreviousRNNs & LSTMsNextAutoencoders & GAN Details