[ИАД, осень 2025] Методы глубокого обучения. Занятие 11: Diffusion Models, Flow Matching

Item: [ИАД, осень 2025] Методы глубокого обучения. Занятие 11: Diffusion Models, Flow Matching
Rating: 4
Author: PostSingularity Institute

by Machine Learning – Intelligent Systems

Formal & Physical Sciences AI — Models & Research UYQArtificial intelligence (AI)UYQFGenerative artificial intelligence

🎙 Machine Learning – Intelligent Systems 👥 8K 📅 November 25, 2025 ⏱ 194 min 👁 230 📄 lecture 🧭 2026-08-16

Available in: English (current) Français

Keywords

diffusionflow matchinggenerative modelsELBOlatent diffusion

Summary

This lecture, part of a course on deep learning methods, focuses on diffusion models and flow matching. The instructor begins with a recap of previous generative models (autoregressive, VAE, GANs) and introduces a taxonomy. The core of the lecture derives diffusion models from the perspective of latent variable models, showing how DDPMs generalize VAEs by introducing a sequence of latent variables following a Markov chain. The forward process (noising) is defined without parameters, while the reverse process (denoising) is parameterized by a neural network. The lecture then covers flow matching as a continuous generalization, discussing its advantages and connection to diffusion. Guidance techniques (classifier-free guidance) are explained as a way to condition generation. Latent diffusion models are presented as a practical approach to reduce computational cost by operating in a lower-dimensional latent space. The lecture concludes with an overview of generative models and a seminar on implementing flow matching on 2D data. The presentation is mathematically rigorous, with derivations of the ELBO and the training objective, and includes practical insights.

170 words

Critical Evaluation

Value of the Information & Strength of the Argument

The lecture provides a comprehensive and rigorous introduction to diffusion models and flow matching, with a strong emphasis on mathematical foundations. The argumentation is solid, building from the ELBO in VAEs to the generalization to DDPMs, and then to flow matching. The instructor clearly explains the intuition behind each step and connects the theory to practical applications. The value lies in the depth of the derivations and the clarity of the presentation, making complex concepts accessible to an advanced audience.

Scientific Rigor, Source Quality, Title Accuracy

The lecture is scientifically rigorous, with careful mathematical derivations and consistent notation. No external sources are cited, but the content aligns with established literature (e.g., Ho et al. 2020, Song et al. 2021, Lipman et al. 2023). The title accurately reflects the content, covering all advertised topics. The lecture is well-structured, with clear sections and a logical flow.

153 words

Title / Content Match

The title accurately reflects the content, covering diffusion models, flow matching, guidance, and latent diffusion models as advertised.

Quality & Reliability

8/10

The lecture is a formal academic presentation by an expert, with rigorous mathematical derivations and clear explanations. No external sources are cited, but the content is consistent with established literature on diffusion models and flow matching.

Chapters

Contribution & Novelties

The lecture provides a clear and detailed derivation of diffusion models from the latent variable perspective, emphasizing the connection to VAEs. It also introduces flow matching as a natural extension, highlighting its practical advantages. The inclusion of guidance and latent diffusion models gives a comprehensive overview of current techniques.

Pour aller plus loin :

Denoising Diffusion Probabilistic Models — The original DDPM paper by Ho et al., foundational for understanding diffusion models.
Flow Matching for Generative Modeling — The paper by Lipman et al. introducing flow matching, a key topic in the lecture.
Classifier-Free Diffusion Guidance — The paper by Ho & Salimans on guidance, directly relevant to the guidance section.

111 words

Radar Profile

The radar profile shows high scores in quantity of information, technical level, and reliability, reflecting the lecture's depth and rigor. The quality of information is also high, but slightly lower due to the lack of external references. Overall, the lecture is a strong educational resource for advanced learners.

Reliability 8/10