The paper proposes Bayesian model averaging and an entropy-minimizing weight optimizer for ensembling foundation models, reporting accuracy gains over output averaging on image and text classification tasks.
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
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Revisiting Bayesian Model Averaging in the Era of Foundation Models
The paper proposes Bayesian model averaging and an entropy-minimizing weight optimizer for ensembling foundation models, reporting accuracy gains over output averaging on image and text classification tasks.