Persistently Trained, Diffusion-assisted Energy-based Models

• URL: http://arxiv.org/abs/2304.10707v1
• Date: Fri, 21 Apr 2023 02:29:18 GMT
• Title: Persistently Trained, Diffusion-assisted Energy-based Models
• Authors: Xinwei Zhang, Zhiqiang Tan, Zhijian Ou
• Abstract summary: We introduce diffusion data and learn a joint EBM, called diffusion assisted-EBMs, through persistent training. We show that persistently trained EBMs can simultaneously achieve long-run stability, post-training image generation, and superior out-of-distribution detection.
• Score: 18.135784288023928
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Maximum likelihood (ML) learning for energy-based models (EBMs) is challenging, partly due to non-convergence of Markov chain Monte Carlo.Several variations of ML learning have been proposed, but existing methods all fail to achieve both post-training image generation and proper density estimation. We propose to introduce diffusion data and learn a joint EBM, called diffusion assisted-EBMs, through persistent training (i.e., using persistent contrastive divergence) with an enhanced sampling algorithm to properly sample from complex, multimodal distributions. We present results from a 2D illustrative experiment and image experiments and demonstrate that, for the first time for image data, persistently trained EBMs can {\it simultaneously} achieve long-run stability, post-training image generation, and superior out-of-distribution detection.

Related papers

• Annealed Langevin Posterior Sampling (ALPS): A Rapid Algorithm for Image Restoration with Multiscale Energy Models [6.5812718077838]
  Energy-Based Models (EBMs) have historically suffered from high computational costs and training instability.<n>We propose Annealed Langevin Posterior Sampling (ALPS) algorithm for Maximum-A-Posteriori (MAP), Minimum Mean Square Error (MMSE) and uncertainty estimates for inverse problems in imaging.
  arXiv  Detail & Related papers  (2026-01-05T22:53:23Z)
• Semi-supervised Image Dehazing via Expectation-Maximization and Bidirectional Brownian Bridge Diffusion Models [29.360740391829935]
  Existing dehazing methods deal with real-world haze images with difficulty, especially scenes with thick haze.<n>We propose an efficient semi-supervised image dehazing method via Expectation-Maximization and Bidirectional Brownian Bridge Diffusion Models (EM-B3DM) with a two-stage learning scheme.
  arXiv  Detail & Related papers  (2025-08-15T02:33:44Z)
• Residual-based Efficient Bidirectional Diffusion Model for Image Dehazing and Haze Generation [17.043633726365233]
  Current deep dehazing methods only focus on removing haze from hazy images, lacking the capability to translate between hazy and haze-free images.<n>We propose a residual-based efficient bidirectional diffusion model (RBDM) that can model the conditional distributions for both dehazing and haze generation.<n>Our RBDM successfully implements size-agnostic bidirectional transitions between haze-free and hazy images with only 15 sampling steps.
  arXiv  Detail & Related papers  (2025-08-15T01:00:15Z)
• Maximum Entropy Inverse Reinforcement Learning of Diffusion Models with Energy-Based Models [12.327318533784961]
  We present a maximum reinforcement learning (IRL) approach for improving the sample quality of diffusion generative models. We train (or fine-tune) a diffusion model using the log density estimated from training data. Our empirical studies show that diffusion models fine-tuned using DxMI can generate high-quality samples in as few as 4 and 10 steps.
  arXiv  Detail & Related papers  (2024-06-30T08:52:17Z)
• EM Distillation for One-step Diffusion Models [65.57766773137068]
  We propose a maximum likelihood-based approach that distills a diffusion model to a one-step generator model with minimal loss of quality. We develop a reparametrized sampling scheme and a noise cancellation technique that together stabilizes the distillation process.
  arXiv  Detail & Related papers  (2024-05-27T05:55:22Z)
• Distilling Diffusion Models into Conditional GANs [90.76040478677609]
  We distill a complex multistep diffusion model into a single-step conditional GAN student model. For efficient regression loss, we propose E-LatentLPIPS, a perceptual loss operating directly in diffusion model's latent space. We demonstrate that our one-step generator outperforms cutting-edge one-step diffusion distillation models.
  arXiv  Detail & Related papers  (2024-05-09T17:59:40Z)
• Iterated Denoising Energy Matching for Sampling from Boltzmann Densities [109.23137009609519]
  Iterated Denoising Energy Matching (iDEM) iDEM alternates between (I) sampling regions of high model density from a diffusion-based sampler and (II) using these samples in our matching objective. We show that the proposed approach achieves state-of-the-art performance on all metrics and trains $2-5times$ faster.
  arXiv  Detail & Related papers  (2024-02-09T01:11:23Z)
• Generalized Contrastive Divergence: Joint Training of Energy-Based Model and Diffusion Model through Inverse Reinforcement Learning [13.22531381403974]
  Generalized Contrastive Divergence (GCD) is a novel objective function for training an energy-based model (EBM) and a sampler simultaneously. We present preliminary yet promising results showing that joint training is beneficial for both EBM and a diffusion model.
  arXiv  Detail & Related papers  (2023-12-06T10:10:21Z)
• Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach [12.623417770432146]
  We present a new method of training energy-based models (EBMs) for anomaly detection that leverages low-dimensional structures within data. The proposed algorithm, Manifold Projection-Diffusion Recovery (MPDR), first perturbs a data point along a low-dimensional manifold that approximates the training dataset. Experimental results show that MPDR exhibits strong performance across various anomaly detection tasks involving diverse data types.
  arXiv  Detail & Related papers  (2023-10-28T11:18:39Z)
• Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood [64.95663299945171]
  Training energy-based models (EBMs) on high-dimensional data can be both challenging and time-consuming. There exists a noticeable gap in sample quality between EBMs and other generative frameworks like GANs and diffusion models. We propose cooperative diffusion recovery likelihood (CDRL), an effective approach to tractably learn and sample from a series of EBMs.
  arXiv  Detail & Related papers  (2023-09-10T22:05:24Z)
• SDDM: Score-Decomposed Diffusion Models on Manifolds for Unpaired Image-to-Image Translation [96.11061713135385]
  This work presents a new score-decomposed diffusion model to explicitly optimize the tangled distributions during image generation. We equalize the refinement parts of the score function and energy guidance, which permits multi-objective optimization on the manifold. SDDM outperforms existing SBDM-based methods with much fewer diffusion steps on several I2I benchmarks.
  arXiv  Detail & Related papers  (2023-08-04T06:21:57Z)
• DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion [144.9653045465908]
  We propose a novel fusion algorithm based on the denoising diffusion probabilistic model (DDPM) Our approach yields promising fusion results in infrared-visible image fusion and medical image fusion.
  arXiv  Detail & Related papers  (2023-03-13T04:06:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.