Generative-Models on My Learning Notes

Generative-Models on My Learning Notes https://learning-notes-dz2.pages.dev/tags/generative-models/ Recent content in Generative-Models on My Learning Notes Hugo -- 0.124.0 en Tue, 16 Jun 2026 07:19:01 +0000 VAE Variants and Modern Interpretations https://learning-notes-dz2.pages.dev/posts/2026-02-25/ Wed, 25 Feb 2026 00:00:00 +0700 https://learning-notes-dz2.pages.dev/posts/2026-02-25/ A survey of where the VAE idea went after 2014 — VQ-VAE, hierarchical VAEs, adversarial hybrids, flow-based posteriors — and what the VAE really gave us beyond a specific architecture. β-VAE and the Emergence of Disentanglement https://learning-notes-dz2.pages.dev/posts/2026-02-10/ Tue, 10 Feb 2026 00:00:00 +0700 https://learning-notes-dz2.pages.dev/posts/2026-02-10/ A single Greek letter in front of the KL term changes what the VAE learns. We look at β-VAE as a rate-distortion trade-off, an information bottleneck, and a simple probe into disentangled representations. Conditional VAE (CVAE): Learning to Generate with Conditions https://learning-notes-dz2.pages.dev/posts/2026-01-25/ Sun, 25 Jan 2026 00:00:00 +0700 https://learning-notes-dz2.pages.dev/posts/2026-01-25/ We extend the VAE into a controllable generative model by adding a condition y into every term of the ELBO. Dissecting the VAE Objective: KL, Reconstruction, and the Reparameterization Trick https://learning-notes-dz2.pages.dev/posts/2026-01-10/ Sat, 10 Jan 2026 00:00:00 +0700 https://learning-notes-dz2.pages.dev/posts/2026-01-10/ We open the ELBO, compute each term, and meet the reparameterization trick — the idea that lets us backpropagate through randomness. Variational Inference: Cracking the Intractable Integral https://learning-notes-dz2.pages.dev/posts/2025-12-20/ Sat, 20 Dec 2025 00:00:00 +0700 https://learning-notes-dz2.pages.dev/posts/2025-12-20/ Variational Inference transforms the impossible task of computing intractable integrals into a solvable optimization problem, providing the mathematical foundation for modern generative models like VAEs. Latent Variable Models: A Probabilistic Foundation https://learning-notes-dz2.pages.dev/posts/2025-10-28/ Tue, 28 Oct 2025 00:00:00 +0700 https://learning-notes-dz2.pages.dev/posts/2025-10-28/ From PCA to Probabilistic PCA and general Latent Variable Models: the probabilistic lens that seeds VAEs. An overview on generative models paradigms https://learning-notes-dz2.pages.dev/posts/2024-12-24/ Tue, 24 Dec 2024 01:12:07 +0700 https://learning-notes-dz2.pages.dev/posts/2024-12-24/ A summary of explicit, implicit and score-based generative models. Diffusion Models https://learning-notes-dz2.pages.dev/posts/2024-06-11/ Tue, 11 Jun 2024 01:12:07 +0700 https://learning-notes-dz2.pages.dev/posts/2024-06-11/ Diffusion Models (DMs) include two processes: forward and backward. Forward process General idea Degrading input data using noise iteratively, forward in time (i.e., $t$ increases). Given image $x_0 \sim q(x_0)$, which called data distribution, forward process gradually adds Gauss noise thru $T$ time steps and produces latent $x_T$. At each time step $t$, we sample Gauss noise that following the distribution $\mathcal{N}(\sqrt{1 - \beta_t} x_{t-1}, \beta_t)$, where the hyper-parameters $0 < \beta_{1:T} < 1$ represent the variance of noise incorporated at each time step.