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The standard recipe applied in transfer learning is to finetune a pretrainedmodel on the task-specific dataset with different hyperparameter settings andpick the model with the highest accuracy on the validation dataset.Unfortunately, this leads to models which do not perform well underdistribution shifts, e.g. when the model is given graphical sketches of theobject as input instead of photos. In order to address this, we propose themanifold mixing model soup, an algorithm which mixes together the latent spacemanifolds of multiple finetuned models in an optimal way in order to generate afused model. We show that the fused model gives significantly betterout-of-distribution performance (+3.5 % compared to best individual model) whenfinetuning a CLIP model for image classification. In addition, it provides alsobetter accuracy on the original dataset where the finetuning has been done.

Do the Frankenstein, or how to achieve better out-of-distribution performance with manifold mixing model soup