Simplified and Generalized Masked Diffusion for Discrete Data

• URL: http://arxiv.org/abs/2406.04329v4
• Date: Thu, 16 Jan 2025 08:46:16 GMT
• Title: Simplified and Generalized Masked Diffusion for Discrete Data
• Authors: Jiaxin Shi, Kehang Han, Zhe Wang, Arnaud Doucet, Michalis K. Titsias,
• Abstract summary: Masked (or absorbing) diffusion is actively explored as an alternative to autoregressive models for generative modeling of discrete data. In this work, we aim to provide a simple and general framework that unlocks the full potential of masked diffusion models.
• Score: 47.711583631408715
• License:
• Abstract: Masked (or absorbing) diffusion is actively explored as an alternative to autoregressive models for generative modeling of discrete data. However, existing work in this area has been hindered by unnecessarily complex model formulations and unclear relationships between different perspectives, leading to suboptimal parameterization, training objectives, and ad hoc adjustments to counteract these issues. In this work, we aim to provide a simple and general framework that unlocks the full potential of masked diffusion models. We show that the continuous-time variational objective of masked diffusion models is a simple weighted integral of cross-entropy losses. Our framework also enables training generalized masked diffusion models with state-dependent masking schedules. When evaluated by perplexity, our models trained on OpenWebText surpass prior diffusion language models at GPT-2 scale and demonstrate superior performance on 4 out of 5 zero-shot language modeling tasks. Furthermore, our models vastly outperform previous discrete diffusion models on pixel-level image modeling, achieving 2.75 (CIFAR-10) and 3.40 (ImageNet 64x64) bits per dimension that are better than autoregressive models of similar sizes. Our code is available at https://github.com/google-deepmind/md4.

Related papers

• ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion Transformer [95.80384464922147]
  Continuous visual generation requires the full-sequence diffusion-based approach. We present ACDiT, an Autoregressive blockwise Conditional Diffusion Transformer. We demonstrate that ACDiT can be seamlessly used in visual understanding tasks despite being trained on the diffusion objective.
  arXiv  Detail & Related papers  (2024-12-10T18:13:20Z)
• Simple and Effective Masked Diffusion Language Models [48.68198363304619]
  We show that simple masked discrete diffusion is more performant than previously thought. Our objective has a simple form -- it is a mixture of classical masked language modeling losses. On language modeling benchmarks, a range of masked diffusion models trained with modern engineering practices achieves a new state-of-the-art.
  arXiv  Detail & Related papers  (2024-06-11T17:51:40Z)
• FreeSeg-Diff: Training-Free Open-Vocabulary Segmentation with Diffusion Models [56.71672127740099]
  We focus on the task of image segmentation, which is traditionally solved by training models on closed-vocabulary datasets. We leverage different and relatively small-sized, open-source foundation models for zero-shot open-vocabulary segmentation. Our approach (dubbed FreeSeg-Diff), which does not rely on any training, outperforms many training-based approaches on both Pascal VOC and COCO datasets.
  arXiv  Detail & Related papers  (2024-03-29T10:38:25Z)
• Adaptive Training Meets Progressive Scaling: Elevating Efficiency in Diffusion Models [52.1809084559048]
  We propose a novel two-stage divide-and-conquer training strategy termed TDC Training. It groups timesteps based on task similarity and difficulty, assigning highly customized denoising models to each group, thereby enhancing the performance of diffusion models. While two-stage training avoids the need to train each model separately, the total training cost is even lower than training a single unified denoising model.
  arXiv  Detail & Related papers  (2023-12-20T03:32:58Z)
• Likelihood-Based Diffusion Language Models [13.916640262862215]
  We take the first steps towards closing the likelihood gap between autoregressive and diffusion-based language models. We pursue this goal through algorithmic improvements, scaling laws, and increased compute. We release Plaid 1B, a large diffusion language model which outperforms GPT-2 124M in likelihood on benchmark datasets.
  arXiv  Detail & Related papers  (2023-05-30T16:43:31Z)
• Diffusion Models as Masked Autoencoders [52.442717717898056]
  We revisit generatively pre-training visual representations in light of recent interest in denoising diffusion models. While directly pre-training with diffusion models does not produce strong representations, we condition diffusion models on masked input and formulate diffusion models as masked autoencoders (DiffMAE) We perform a comprehensive study on the pros and cons of design choices and build connections between diffusion models and masked autoencoders.
  arXiv  Detail & Related papers  (2023-04-06T17:59:56Z)

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.