Reimagining Parameter Space Exploration with Diffusion Models
This work addresses the time-consuming and data-reliant issue of task-specific fine-tuning for neural networks, though it is incremental as it builds on existing diffusion model techniques.
The authors tackled the problem of adapting neural networks to new tasks without task-specific fine-tuning by using diffusion models to generate task-specific parameters directly from task identifiers. Experiments showed that diffusion models can generate accurate parameters for seen tasks and support multi-task interpolation, but fail to generalize to unseen tasks.
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.