Summary of Maximum Entropy Inverse Reinforcement Learning Of Diffusion Models with Energy-based Models, by Sangwoong Yoon et al.
Maximum Entropy Inverse Reinforcement Learning of Diffusion Models with Energy-Based Models
by Sangwoong Yoon, Himchan Hwang, Dohyun Kwon, Yung-Kyun Noh, Frank C. Park
First submitted to arxiv on: 30 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a maximum entropy inverse reinforcement learning (IRL) approach to improve the sample quality of diffusion generative models, particularly when the number of generation time steps is small. The authors propose two methods: Diffusion by Maximum Entropy IRL (DxMI), which trains a diffusion model using the log probability density estimated from training data, and Diffusion by Dynamic Programming (DxDP), a novel reinforcement learning algorithm for diffusion models that enables efficient updates. The DxMI formulation is a minimax problem that reaches equilibrium when both models converge to the data distribution. The entropy maximization plays a key role in DxMI, facilitating exploration and ensuring convergence. The authors demonstrate the effectiveness of their approach through empirical studies, showing that fine-tuned diffusion models can generate high-quality samples in as few as 4 and 10 steps. Additionally, DxMI enables the training of an energy-based model (EBM) without Markov chain Monte Carlo (MCMC), stabilizing EBM training dynamics and enhancing anomaly detection performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper improves the quality of diffusion generative models by using maximum entropy inverse reinforcement learning. This approach trains a diffusion model to generate samples that are similar to the data it was trained on. The authors also propose a new algorithm, Diffusion by Dynamic Programming, which helps the diffusion model update its parameters more efficiently. They show that their approach can be used to generate high-quality samples in just a few steps, and that it can even help train an energy-based model without needing complex algorithms like Markov chain Monte Carlo. |
Keywords
* Artificial intelligence * Anomaly detection * Diffusion * Diffusion model * Energy based model * Probability * Reinforcement learning
