DT-UFC: Universal Large Model Feature Coding via Peaky-to-Balanced Distribution Transformation

• URL: http://arxiv.org/abs/2506.16495v1
• Date: Thu, 19 Jun 2025 17:43:32 GMT
• Title: DT-UFC: Universal Large Model Feature Coding via Peaky-to-Balanced Distribution Transformation
• Authors: Changsheng Gao, Zijie Liu, Li Li, Dong Liu, Xiaoyan Sun, Weisi Lin,
• Abstract summary: We present the first systematic study on universal feature coding for large models.<n>Key challenge lies in the inherently diverse and distributionally incompatible nature of features extracted from different models.<n>We propose a learned peaky-to-balanced distribution transformation, which reshapes highly skewed feature distributions into a common, balanced target space.
• Score: 43.46266352100794
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Like image coding in visual data transmission, feature coding is essential for the distributed deployment of large models by significantly reducing transmission and storage overhead. However, prior studies have mostly targeted task- or model-specific scenarios, leaving the challenge of universal feature coding across diverse large models largely unaddressed. In this paper, we present the first systematic study on universal feature coding for large models. The key challenge lies in the inherently diverse and distributionally incompatible nature of features extracted from different models. For example, features from DINOv2 exhibit highly peaky, concentrated distributions, while those from Stable Diffusion 3 (SD3) are more dispersed and uniform. This distributional heterogeneity severely hampers both compression efficiency and cross-model generalization. To address this, we propose a learned peaky-to-balanced distribution transformation, which reshapes highly skewed feature distributions into a common, balanced target space. This transformation is non-uniform, data-driven, and plug-and-play, enabling effective alignment of heterogeneous distributions without modifying downstream codecs. With this alignment, a universal codec trained on the balanced target distribution can effectively generalize to features from different models and tasks. We validate our approach on three representative large models-LLaMA3, DINOv2, and SD3-across multiple tasks and modalities. Extensive experiments show that our method achieves notable improvements in both compression efficiency and cross-model generalization over task-specific baselines. All source code will be released for future research.

Related papers

• DiffSG: A Generative Solver for Network Optimization with Diffusion Model [75.27274046562806]
  Generative diffusion models are popular in various cross-domain applications.<n>These models hold promise in tackling complex network optimization problems.<n>We propose a new framework for generative diffusion models called Diffusion Model-based Solution Generation.
  arXiv  Detail & Related papers  (2024-08-13T07:56:21Z)
• Improving Generalization of Neural Vehicle Routing Problem Solvers Through the Lens of Model Architecture [9.244633039170186]
  We propose a plug-and-play Entropy-based Scaling Factor (ESF) and a Distribution-Specific (DS) decoder.<n>ESF adjusts the attention weight pattern of the model towards familiar ones discovered during training when solving VRPs of varying sizes.<n>DS decoder explicitly models VRPs of multiple training distribution patterns through multiple auxiliary light decoders, expanding the model representation space.
  arXiv  Detail & Related papers  (2024-06-10T09:03:17Z)
• Selective Hourglass Mapping for Universal Image Restoration Based on Diffusion Model [36.57703763466984]
  We propose an advanced selective hourglass mapping strategy based on diffusion model, DiffUIR. We achieve state-of-the-art performance on five image restoration tasks, 22 benchmarks in the universal setting and zero-shot generalization setting.
  arXiv  Detail & Related papers  (2024-03-17T09:41:20Z)
• Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-Decoding [90.77521413857448]
  Deep generative models are anchored in three core capabilities -- generating new instances, reconstructing inputs, and learning compact representations. We introduce Generalized generative adversarial-Decoding Diffusion Probabilistic Models (EDDPMs) EDDPMs generalize the Gaussian noising-denoising in standard diffusion by introducing parameterized encoding-decoding. Experiments on text, proteins, and images demonstrate the flexibility to handle diverse data and tasks.
  arXiv  Detail & Related papers  (2024-02-29T10:08:57Z)
• FedUV: Uniformity and Variance for Heterogeneous Federated Learning [5.9330433627374815]
  Federated learning is a promising framework to train neural networks with widely distributed data. Recent work has shown this is due to the final layer of the network being most prone to local bias. We investigate the training dynamics of the classifier by applying SVD to the weights motivated by the observation that freezing weights results in constant singular values.
  arXiv  Detail & Related papers  (2024-02-27T15:53:15Z)
• Complexity Matters: Rethinking the Latent Space for Generative Modeling [65.64763873078114]
  In generative modeling, numerous successful approaches leverage a low-dimensional latent space, e.g., Stable Diffusion. In this study, we aim to shed light on this under-explored topic by rethinking the latent space from the perspective of model complexity.
  arXiv  Detail & Related papers  (2023-07-17T07:12:29Z)
• Dior-CVAE: Pre-trained Language Models and Diffusion Priors for Variational Dialog Generation [70.2283756542824]
  Dior-CVAE is a hierarchical conditional variational autoencoder (CVAE) with diffusion priors to address these challenges. We employ a diffusion model to increase the complexity of the prior distribution and its compatibility with the distributions produced by a PLM. Experiments across two commonly used open-domain dialog datasets show that our method can generate more diverse responses without large-scale dialog pre-training.
  arXiv  Detail & Related papers  (2023-05-24T11:06:52Z)
• Hierarchical Integration Diffusion Model for Realistic Image Deblurring [71.76410266003917]
  Diffusion models (DMs) have been introduced in image deblurring and exhibited promising performance. We propose the Hierarchical Integration Diffusion Model (HI-Diff), for realistic image deblurring. Experiments on synthetic and real-world blur datasets demonstrate that our HI-Diff outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-22T12:18:20Z)
• The Score-Difference Flow for Implicit Generative Modeling [1.1929584800629673]
  Implicit generative modeling aims to produce samples of synthetic data matching a target data distribution.<n>Recent work has approached the IGM problem from the perspective of pushing synthetic source data toward the target distribution.<n>We present the score difference between arbitrary target and source distributions as a flow that optimally reduces the Kullback-Leibler divergence between them.
  arXiv  Detail & Related papers  (2023-04-25T15:21:12Z)
• One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale [36.590918776922905]
  This paper proposes a unified diffusion framework (dubbed UniDiffuser) to fit all distributions relevant to a set of multi-modal data in one model. Inspired by the unified view, UniDiffuser learns all distributions simultaneously with a minimal modification to the original diffusion model.
  arXiv  Detail & Related papers  (2023-03-12T03:38:39Z)

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.