On tuning consistent annealed sampling for denoising score matching

• URL: http://arxiv.org/abs/2104.03725v1
• Date: Thu, 8 Apr 2021 12:19:10 GMT
• Title: On tuning consistent annealed sampling for denoising score matching
• Authors: Joan Serr\`a, Santiago Pascual, Jordi Pons
• Abstract summary: Score-based generative models provide state-of-the-art quality for image and audio synthesis. In this note, we first overview three common sampling schemes for models trained with denoising score matching.
• Score: 17.10144603522758
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Score-based generative models provide state-of-the-art quality for image and audio synthesis. Sampling from these models is performed iteratively, typically employing a discretized series of noise levels and a predefined scheme. In this note, we first overview three common sampling schemes for models trained with denoising score matching. Next, we focus on one of them, consistent annealed sampling, and study its hyper-parameter boundaries. We then highlight a possible formulation of such hyper-parameter that explicitly considers those boundaries and facilitates tuning when using few or a variable number of steps. Finally, we highlight some connections of the formulation with other sampling schemes.

Related papers

• Unified Convergence Analysis for Score-Based Diffusion Models with Deterministic Samplers [49.1574468325115]
  We introduce a unified convergence analysis framework for deterministic samplers. Our framework achieves iteration complexity of $tilde O(d2/epsilon)$. We also provide a detailed analysis of Denoising Implicit Diffusion Models (DDIM)-type samplers.
  arXiv  Detail & Related papers  (2024-10-18T07:37:36Z)
• Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control [26.195547996552406]
  We cast reward fine-tuning as optimal control (SOC) for dynamical generative models that produce samples through an iterative process. We find that our approach significantly improves over existing methods for reward fine-tuning, achieving better consistency, realism, and generalization to unseen human preference reward models.
  arXiv  Detail & Related papers  (2024-09-13T14:22:14Z)
• On Robust Learning from Noisy Labels: A Permutation Layer Approach [53.798757734297986]
  This paper introduces a permutation layer learning approach termed PermLL to dynamically calibrate the training process of a deep neural network (DNN) We provide two variants of PermLL in this paper: one applies the permutation layer to the model's prediction, while the other applies it directly to the given noisy label. We validate PermLL experimentally and show that it achieves state-of-the-art performance on both real and synthetic datasets.
  arXiv  Detail & Related papers  (2022-11-29T03:01:48Z)
• From Denoising Diffusions to Denoising Markov Models [38.33676858989955]
  Denoising diffusions are state-of-the-art generative models exhibiting remarkable empirical performance. We propose a unifying framework generalising this approach to a wide class of spaces and leading to an original extension of score matching.
  arXiv  Detail & Related papers  (2022-11-07T14:34:27Z)
• Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics [13.097161185372151]
  We propose a sampling procedure that initializes a sequence with white noise and follows a Markov chain defined by Langevin dynamics on the global log-likelihood of the sequence. We apply these techniques to autoregressive models in the visual and audio domains, with competitive results for audio source separation, super-resolution, and inpainting.
  arXiv  Detail & Related papers  (2021-05-17T21:07:02Z)
• Noise Estimation for Generative Diffusion Models [91.22679787578438]
  In this work, we present a simple and versatile learning scheme that can adjust the noise parameters for any given number of steps. Our approach comes at a negligible computation cost.
  arXiv  Detail & Related papers  (2021-04-06T15:46:16Z)
• Jo-SRC: A Contrastive Approach for Combating Noisy Labels [58.867237220886885]
  We propose a noise-robust approach named Jo-SRC (Joint Sample Selection and Model Regularization based on Consistency) Specifically, we train the network in a contrastive learning manner. Predictions from two different views of each sample are used to estimate its "likelihood" of being clean or out-of-distribution.
  arXiv  Detail & Related papers  (2021-03-24T07:26:07Z)
• Learning Noise-Aware Encoder-Decoder from Noisy Labels by Alternating Back-Propagation for Saliency Detection [54.98042023365694]
  We propose a noise-aware encoder-decoder framework to disentangle a clean saliency predictor from noisy training examples. The proposed model consists of two sub-models parameterized by neural networks.
  arXiv  Detail & Related papers  (2020-07-23T18:47:36Z)
• Generating diverse and natural text-to-speech samples using a quantized fine-grained VAE and auto-regressive prosody prior [53.69310441063162]
  This paper proposes a sequential prior in a discrete latent space which can generate more naturally sounding samples. We evaluate the approach using listening tests, objective metrics of automatic speech recognition (ASR) performance, and measurements of prosody attributes.
  arXiv  Detail & Related papers  (2020-02-06T12:35:50Z)

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.