Generative Modeling, Diffusion, and Representation Learning
Representation Learning
Latent spaces
Representation learning aims to map high-dimensional observations into useful latent spaces where downstream tasks become easier. Autoencoders, variational autoencoders, contrastive methods, and self-supervised learning all pursue this goal from different angles. The quality of a representation is often measured by transfer performance rather than reconstruction alone.
Why are learned representations useful?
Good representations capture structure that can be reused across tasks.
Correct answer: They can simplify downstream tasks and transfer knowledge
What is one benefit of self-supervised learning?
Self-supervision creates training signals from the data itself.
Correct answer: It leverages unlabeled data to learn useful features.
Generative Models and Diffusion
Modern generative modeling
Generative models learn to sample from a data distribution. Variational methods optimize a lower bound on likelihood, adversarial methods learn through a game between generator and discriminator, and diffusion models learn iterative denoising processes. In TensorFlow, these models often require custom training loops, careful noise schedules, and domain-specific evaluation.
Three generative paradigms
VAE
- Explicit latent-variable framework
- Often smooth latent space but blurrier samples
GAN
- Sharp samples and adversarial training
- Can be unstable and mode-collapsing
Evaluation warning
Image quality metrics alone may not capture diversity, fidelity, or task usefulness of generative models.
What is a common training idea in diffusion models?
Diffusion models learn to reverse a noise process through successive refinement.
Correct answer: Iterative denoising of noisy samples
Name one reason GAN training can be unstable.
The minimax game can lead to oscillation or mode collapse.
Correct answer: Because generator and discriminator dynamics can become unbalanced.