Variational Autoencoders (VAEs)

Introduction

• Excitement & Applications:
  • Enables cool applications; e.g., altering Mona Lisa's expressions and accessories by changing latent vectors.

Basic Concepts

Autoencoder Recap

• Autoencoder: Encodes input data to smaller space and decodes back to original.
  • Usage: Reduce large files, denoise images, anomaly detection, domain adaptation, image colorization.
  • Mechanism: Training by minimizing reconstruction error; app. domains use tricks like input/output dependent processing for noise reduction, etc.
  • Variants: Convolutional, LSTM

Generative Adversarial Networks (GANs) vs. VAEs

• GANs: Separate generator and discriminator networks competing against each other to generate new images.
• VAEs: Aim to create a generative network but with different principles; sampling from latent spaces to generate new data.
  • Latent Vector Challenges in Standard Autoencoders: Random sampling may yield meaningless results.
  • Key in VAEs: Picking appropriate latent vectors to ensure meaningful generation.

Variational Autoencoders

Concept & Mechanism

• Latent Space: Constrains latent vector values to a continuous region for varied image outputs.
  • MNIST Example: Each number (0-9) represented in latent space.
• Distribution Mapping: Instead of fixed latent vectors, map to distributions (e.g., normal distribution).
• Forcing Normal Distribution: Use mean and standard deviation as parameters instead of entire encoder output.
  • KL Divergence: Quantifies distance between learned distribution and standard normal distribution; used as a loss function.
  • Loss Functions: Reconstruction loss + KL Divergence.

Technical Insights

• **Latent Vector Definition: **Instead of a fixed vector, use a sampled latent vector using mean and standard deviation.
  • Stochastic Z: Random sampling within constrained regions defined by mean & standard deviation.
• Back Propagation: Uses re-parameterization trick for back propagation in stochastic sampling.
  • Parameter Learning: Mean and standard deviation values are learned; epsilon (sampled from fixed normal distribution) isn't learned.

Practical Application

• Code Implementation: Next video on implementing VAEs with Keras and MNIST dataset.

Final Notes

• Importance: Constrained latent space with meaningful vectors essential for generative applications.
• Encouragement: Subscribe for more detailed follow-ups and practical applications.