用扩散模型直接生成任务专属参数，免去调优烦恼。
Reimagining Parameter Space Exploration with Diffusion Models
- 用扩散模型学习任务参数空间结构，按任务名生成权重。
- 对已见任务生成参数准确率高，支持多任务插值。
- 无法泛化到未见任务，适合已有任务的快速部署。
将神经网络适配新任务通常需要针对任务进行微调，耗时且依赖标注数据。我们探索一种生成式替代方案，直接从任务标识生成特定任务的参数，无需任务特定训练。为此，我们提出使用扩散模型学习有效任务特定参数空间的底层结构，并按需合成参数。训练完成后，任务条件扩散模型可直接从任务标识生成专用权重。我们在三种场景下评估该方法：为单一已见任务生成参数、为多个已见任务生成参数，以及为完全未见任务生成参数。实验表明，扩散模型能生成准确的任务特定参数，在参数子空间结构良好时支持多任务插值，但无法泛化到未见任务，揭示了该生成式方案的潜力与局限。
原文摘要 · Abstract (English)
Adapting neural networks to new tasks typically requires task-specific fine-tuning, which is time-consuming and reliant on labeled data. We explore a generative alternative that produces task-specific parameters directly from task identity, eliminating the need for task-specific training. To this end, we propose using diffusion models to learn the underlying structure of effective task-specific parameter space and synthesize parameters on demand. Once trained, the task-conditioned diffusion model can generate specialized weights directly from task identifiers. We evaluate this approach across three scenarios: generating parameters for a single seen task, for multiple seen tasks, and for entirely unseen tasks. Experiments show that diffusion models can generate accurate task-specific parameters and support multi-task interpolation when parameter subspaces are well-structured, but fail to generalize to unseen tasks, highlighting both the potential and limitations of this generative solution.
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