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Found 5 papers, 4 papers with code
Merging Multi-Task Models via Weight-Ensembling Mixture of Experts
A notable challenge is mitigating the interference between parameters of different models, which can substantially deteriorate performance.
Concrete Subspace Learning based Interference Elimination for Multi-task Model Fusion
At the upper level, we focus on learning a shared Concrete mask to identify the subspace, while at the inner level, model merging is performed to maximize the performance of the merged model.
Learn From Model Beyond Fine-Tuning: A Survey
LFM focuses on the research, modification, and design of FM based on the model interface, so as to better understand the model structure and weights (in a black box environment), and to generalize the model to downstream tasks.
Parameter Efficient Multi-task Model Fusion with Partial Linearization
We demonstrate that our partial linearization technique enables a more effective fusion of multiple tasks into a single model, outperforming standard adapter tuning and task arithmetic alone.
Improving Heterogeneous Model Reuse by Density Estimation
This paper studies multiparty learning, aiming to learn a model using the private data of different participants.
