Short Run Langevin Flow

Short-run Langevin flow leverages the principles of Langevin dynamics, a type of Markov Chain Monte Carlo (MCMC) method, to perform approximate inference within generative models, often in conjunction with normalizing flows. Current research focuses on cooperative learning frameworks where short-run Langevin flow is combined with normalizing flows to improve the generation of realistic samples and enhance model training efficiency, particularly for energy-based models. This approach addresses challenges in approximating intractable posterior distributions and offers a powerful tool for various applications, including image generation, reconstruction, and anomaly detection. The resulting hybrid models demonstrate improved performance compared to using either method alone.