{"@attributes":{"version":"2.0"},"channel":{"title":"Denoising-Diffusion-Models on Oriol Al\u00e0s Cerc\u00f3s","link":"https:\/\/oriolac.github.io\/tags\/denoising-diffusion-models\/","description":"Recent content in Denoising-Diffusion-Models on Oriol Al\u00e0s Cerc\u00f3s","generator":"Hugo -- 0.150.0","language":"en-us","copyright":"Oriol Al\u00e0s Cerc\u00f3s","lastBuildDate":"Thu, 10 Jul 2025 12:13:48 +0100","item":{"title":"The Generative Trilemma: A quick overview","link":"https:\/\/oriolac.github.io\/posts\/20250710-starting-diffusion\/","pubDate":"Thu, 10 Jul 2025 12:13:48 +0100","guid":"https:\/\/oriolac.github.io\/posts\/20250710-starting-diffusion\/","description":"<p>Generative models are a class of machine learning that learn a representation of the data trained on and they model the\ndata itself.<\/p>\n<p>Ideally, generative models should satisfy the following key requirements in a real environment:<\/p>\n<ul>\n<li><strong>High quality samples<\/strong> refers to those samples that captures the underlying patterns and\nstructures present in the data making them indistinguishable from human observers.<\/li>\n<li><strong>Fast Sampling<\/strong> is about the efficiency of image generation and the computational overhead\nthat can cause generative models.<\/li>\n<li><strong>Mode Coverage\/Diversity<\/strong> points out how the model is able to generate a full range of\nmods and diverse patterns present in the training data<\/li>\n<\/ul>\n<p>\n<figure>\n<img loading=\"lazy\" src=\"https:\/\/oriolac.github.io\/posts\/2025\/gen_tril\/gen_tril.png#center\" alt=\"alt text\" title=\"Fig. 1. The Generative Learning Trilemma\" \/>\n<figcaption\nstyle=\"\nfont-size: 15px;\ncolor: #7a7a7a;\nmargin-top: 0.5em;\ntext-align: center;\nfont-weight: 100;\n\"\n>\nFig. 1. The Generative Learning Trilemma\n<\/figcaption>\n<\/figure>\n<\/p>"}}}