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Lecture 4 of MIT 6.S191 2026 is Generative Modeling: From Latent Spaces to Diffusion. It Separates generative from discriminative tasks, organizes VAE and GAN objectives (this lecture's video does not cover diffusion; the speaker defers it to Lecture 6), and leads into Lab 2’s DB-VAE. This note uses only the official 2026 slides and video; it does not mix in similarly named material from 2025.
What to take away
- Explain how latent variables can represent factors of variation
- Distinguish reconstruction and adversarial objectives (denoising/diffusion is covered in the Lecture 6 video)
- Recognize that sample quality does not imply that data bias is gone
These goals have one thing in common: recognizing terminology is insufficient. You should be able to identify inputs, outputs, the learning signal, and the main constraint before moving on.
A VAE encodes an input as a distribution, samples a latent value, and decodes a reconstruction. A GAN pits a generator against a discriminator, while diffusion learns to reverse a noising process (this part is not in this lecture's video; see Lecture 6). Because their objectives differ, “better generation” must be defined in terms of fidelity, diversity, and downstream use.
How to watch
First scan the sections and diagrams in the official slides, then watch the official video. On a second pass, pause at equations and architecture diagrams and redraw them in your own notation. Afterward, close the material and write three central ideas plus one unresolved question.
An exercise for tonight
Draw a VAE with encoder, sampling, and decoder; label both loss terms before opening Lab 2 Part 2.
“Finished” means leaving a checkable diagram, calculation, program output, or short note—not merely reaching the end of the video. You should also be able to explain one failure mode to someone else.
Scope and limits
6.S191 is a high-intensity introduction, and this article is only a lecture guide. It does not replace the full recording, rigorous derivations, or instructor feedback. Use a semester course or primary papers when a topic needs theoretical depth.
Course video sources
Rechecked against the live official course page on 2026-10-10: the lecture numbers and recording links match and the videos are public and embeddable.
Original videos: MIT 6.S191: Deep Generative Modeling
Course and recording entries:
Checked: 2026-10-10.
Content check: verified against the video transcript (2026-10-10): the transcript covers supervised versus unsupervised learning, density estimation and sample generation, autoencoders, VAEs (mean and variance, KL regularization, reparameterization), GANs (generator and discriminator) and CycleGAN, and says the day's lab uses a VAE to debias face detection. The transcript does not cover diffusion: the speaker says diffusion comes in "tomorrow's lecture six", so this note claiming diffusion and denoising as content of this video did not hold and now points to the Lecture 6 video.
Update Log
- 2026-10-10: Added explicit video status and checked recording sources and access notes.
- 2026-10-10: Rechecked video status. Live-checked the official course page: lecture numbers and recording links match and the videos are public, so the status is now “Videos included.”
- 2026-10-10: Checked the video content against its transcript. The transcript does not cover diffusion (the speaker defers it to Lecture 6), so the passages that attributed diffusion and denoising to this video were corrected; the frontmatter title and tldr still mention diffusion and were left unchanged.
References
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