DCDB: Dynamic Conditional Dual Diffusion Bridge for Ill-posed Multi-Tasks

• URL: http://arxiv.org/abs/2509.03044v2
• Date: Sat, 08 Nov 2025 16:35:53 GMT
• Title: DCDB: Dynamic Conditional Dual Diffusion Bridge for Ill-posed Multi-Tasks
• Authors: Chengjie Huang, Jiafeng Yan, Jing Li, Lu Bai,
• Abstract summary: We propose a dynamic conditional double diffusion bridge training paradigm to build a general framework for ill-posed multi-tasks.<n>We analyze the learning objectives of the network under different conditional forms in the single-step denoising process.<n>We achieve the best performance in multiple indicators on public datasets.
• Score: 6.156822646166696
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conditional diffusion models have made impressive progress in the field of image processing, but the characteristics of constructing data distribution pathways make it difficult to exploit the intrinsic correlation between tasks in multi-task scenarios, which is even worse in ill-posed tasks with a lack of training data. In addition, traditional static condition control makes it difficult for networks to learn in multi-task scenarios with its dynamically evolving characteristics. To address these challenges, we propose a dynamic conditional double diffusion bridge training paradigm to build a general framework for ill-posed multi-tasks. Firstly, this paradigm decouples the diffusion and condition generation processes, avoiding the dependence of the diffusion model on supervised data in ill-posed tasks. Secondly, generated by the same noise schedule, dynamic conditions are used to gradually adjust their statistical characteristics, naturally embed time-related information, and reduce the difficulty of network learning. We analyze the learning objectives of the network under different conditional forms in the single-step denoising process and compare the changes in its attention weights in the network, demonstrating the superiority of our dynamic conditions. Taking dehazing and visible-infrared fusion as typical ill-posed multi-task scenarios, we achieve the best performance in multiple indicators on public datasets. The code has been publicly released at: https://anonymous.4open.science/r/DCDB-D3C2.

Related papers

• Packet-Level DDoS Data Augmentation Using Dual-Stream Temporal-Field Diffusion [2.8498570090658726]
  In response to Distributed Denial of Service (DDoS) attacks, recent research efforts increasingly rely on Machine Learning (ML)-based solutions.<n>Current synthetic trace generation methods struggle to capture the complex temporal patterns and spatial distributions exhibited in emerging DDoS attacks.<n>We propose Dual-Stream Temporal-Field Diffusion (DSTF-Diffusion), a multi-view, multi-stream network traffic generative model based on diffusion models.
  arXiv  Detail & Related papers  (2025-07-27T03:40:56Z)
• MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings [75.0617088717528]
  MoCa is a framework for transforming pre-trained VLM backbones into effective bidirectional embedding models.<n>MoCa consistently improves performance across MMEB and ViDoRe-v2 benchmarks, achieving new state-of-the-art results.
  arXiv  Detail & Related papers  (2025-06-29T06:41:00Z)
• Structural-Temporal Coupling Anomaly Detection with Dynamic Graph Transformer [41.16574023720132]
  We propose a structural-temporal coupling anomaly detection architecture with a dynamic graph transformer model.<n>Specifically, we introduce structural and temporal features from two integration levels to provide anomaly-aware graph evolutionary patterns.
  arXiv  Detail & Related papers  (2025-05-13T08:10:41Z)
• UniSTD: Towards Unified Spatio-Temporal Learning across Diverse Disciplines [64.84631333071728]
  We introduce bfUnistage, a unified Transformer-based framework fortemporal modeling.<n>Our work demonstrates that a task-specific vision-text can build a generalizable model fortemporal learning.<n>We also introduce a temporal module to incorporate temporal dynamics explicitly.
  arXiv  Detail & Related papers  (2025-03-26T17:33:23Z)
• Underlying Semantic Diffusion for Effective and Efficient In-Context Learning [113.4003355229632]
  Underlying Semantic Diffusion (US-Diffusion) is an enhanced diffusion model that boosts underlying semantics learning, computational efficiency, and in-context learning capabilities.<n>We present a Feedback-Aided Learning (FAL) framework, which leverages feedback signals to guide the model in capturing semantic details.<n>We also propose a plug-and-play Efficient Sampling Strategy (ESS) for dense sampling at time steps with high-noise levels.
  arXiv  Detail & Related papers  (2025-03-06T03:06:22Z)
• Dynamical Diffusion: Learning Temporal Dynamics with Diffusion Models [71.63194926457119]
  We introduce Dynamical Diffusion (DyDiff), a theoretically sound framework that incorporates temporally aware forward and reverse processes.<n>Experiments across scientifictemporal forecasting, video prediction, and time series forecasting demonstrate that Dynamical Diffusion consistently improves performance in temporal predictive tasks.
  arXiv  Detail & Related papers  (2025-03-02T16:10:32Z)
• Causal Time-Series Synchronization for Multi-Dimensional Forecasting [1.1060425537315088]
  The process industry's high expectations for Digital Twins require modeling approaches that can generalize across tasks and diverse domains. Our approach focuses on: (i) identifying highly lagged causal relationships using data-driven methods, (ii) synchronizing cause-effect pairs to generate training samples for channel-dependent pre-training, and (iii) evaluating the effectiveness of this approach in channel-dependent forecasting.
  arXiv  Detail & Related papers  (2024-11-15T12:50:57Z)
• T-SaS: Toward Shift-aware Dynamic Adaptation for Streaming Data [9.829993835712422]
  This paper aims to solve the problem of sequential data modeling in the presence of sudden distribution shifts. Specifically, we design a Bayesian framework, dubbed as T-SaS, with a discrete distribution-modeling variable to capture abrupt shifts of data. The proposed method learns specific model parameters for each distribution by learning which neurons should be activated in the full network.
  arXiv  Detail & Related papers  (2023-09-05T22:55:10Z)
• Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning [101.66860222415512]
  Multi-Task Diffusion Model (textscMTDiff) is a diffusion-based method that incorporates Transformer backbones and prompt learning for generative planning and data synthesis. For generative planning, we find textscMTDiff outperforms state-of-the-art algorithms across 50 tasks on Meta-World and 8 maps on Maze2D.
  arXiv  Detail & Related papers  (2023-05-29T05:20:38Z)
• Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment [55.91291559442884]
  This work develops a methodology that enables data-driven methods to continuously learn and optimize in a dynamic environment. We propose to build the notion of continual learning into the modeling process of learning wireless systems. Our design is based on a novel min-max formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2020-11-16T08:24:34Z)

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.