Part 6: Generative Models
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jshn9515
2026-05-05
2026-05-21
| Title | Author | Date |
|---|---|---|
| 12.1 Generative Adversarial Network (GAN) | jshn9515 | 2026-03-19 |
| 13.1 AutoEncoder: Starting with Compression and Reconstruction | jshn9515 | 2026-03-24 |
| 13.2 VAE: Probabilistic Modeling and the Reparameterization Trick | jshn9515 | 2026-03-25 |
| 13.3 ELBO: Where Does the VAE Objective Function Come From? | jshn9515 | 2026-03-25 |
| 13.4 VAE Training Phenomena and Latent Space Intuition | jshn9515 | 2026-03-27 |
| 13.5 Advantages, Limitations, and Future Developments | jshn9515 | 2026-03-28 |
| 14.1 DDPM: From Denoising to Generation | jshn9515 | 2026-03-29 |
| 14.2 The Forward Process of DDPM: From Image to Noise | jshn9515 | 2026-03-31 |
| 14.3 DDPM’s Reverse Denoising Process and Training Objective | jshn9515 | 2026-03-31 |
| 14.4 DDPM Network Structure and Sampling Process | jshn9515 | 2026-04-01 |
| 14.5 DDPM from a Variational Derivation: Where Does the ELBO Come From? | jshn9515 | 2026-04-02 |