# Learning to Generate Samples from Noise through Infusion Training

arXiv: https://arxiv.org/abs/1703.06975 · HTML: https://mlsanity.com/paper/1703.06975/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2017.

## Built on by (most cited first, 15 of 15)

- [Tackling the Generative Learning Trilemma with Denoising Diffusion GANs](https://mlsanity.com/paper/2112.07804/) (2021)
- [Score-Based Generative Modeling with Critically-Damped Langevin Diffusion](https://mlsanity.com/paper/2112.07068/) (2021)
- [Score-Based Generative Models Detect Manifolds](https://mlsanity.com/paper/2206.01018/) (2022)
- [Diffusion Normalizing Flow](https://mlsanity.com/paper/2110.07579/) (2021)
- [Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders](https://mlsanity.com/paper/2202.09671/) (2022)
- [Learning to Generate 3D Shapes with Generative Cellular Automata](https://mlsanity.com/paper/2103.04130/) (2021)
- [Fast Sampling via Discrete Non-Markov Diffusion Models with Predetermined Transition Time](https://mlsanity.com/paper/2312.09193/) (2023)
- [Probabilistic Implicit Scene Completion](https://mlsanity.com/paper/2204.01264/) (2022)
- [AutoLoRA: AutoGuidance Meets Low-Rank Adaptation for Diffusion Models](https://mlsanity.com/paper/2410.03941/) (2024)
- [Policy Gradient With Serial Markov Chain Reasoning](https://mlsanity.com/paper/2210.06766/) (2022)
- [Outdoor Scene Extrapolation with Hierarchical Generative Cellular Automata](https://mlsanity.com/paper/2406.08292/) (2024)
- [Combinatorial Complex Score-based Diffusion Modelling through Stochastic Differential Equations](https://mlsanity.com/paper/2406.04916/) (2024)
- [Diffusion Model from Scratch](https://mlsanity.com/paper/2412.10824/) (2024)
- [ExCellGen: Fast, Controllable, Photorealistic 3D Scene Generation from a Single Real-World Exemplar](https://mlsanity.com/paper/2412.16253/) (2024)
- [TFDM: Time-Variant Frequency-Based Point Cloud Diffusion with Mamba](https://mlsanity.com/paper/2503.13004/) (2025)

## Abstract (arXiv)

In this work, we investigate a novel training procedure to learn a generative model as the transition operator of a Markov chain, such that, when applied repeatedly on an unstructured random noise sample, it will denoise it into a sample that matches the target distribution from the training set. The novel training procedure to learn this progressive denoising operation involves sampling from a slightly different chain than the model chain used for generation in the absence of a denoising target. In the training chain we infuse information from the training target example that we would like the chains to reach with a high probability. The thus learned transition operator is able to produce quality and varied samples in a small number of steps. Experiments show competitive results compared to the samples generated with a basic Generative Adversarial Net