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Model soups to increase inference without increasing compute time

2023-01-24 15:59:07
Charles Dansereau, Milo Sobral, Maninder Bhogal, Mehdi Zalai

Abstract

In this paper, we compare Model Soups performances on three different models (ResNet, ViT and EfficientNet) using three Soup Recipes (Greedy Soup Sorted, Greedy Soup Random and Uniform soup) from arXiv:2203.05482, and reproduce the results of the authors. We then introduce a new Soup Recipe called Pruned Soup. Results from the soups were better than the best individual model for the pre-trained vision transformer, but were much worst for the ResNet and the EfficientNet. Our pruned soup performed better than the uniform and greedy soups presented in the original paper. We also discuss the limitations of weight-averaging that were found during the experiments. The code for our model soup library and the experiments with different models can be found here: this https URL

Abstract (translated)

在本文中,我们比较了模型汤在不同模型上的性能,包括ResNet、ViT和EfficientNet。我们使用了arXiv:2203.05482中的三个汤 recipe(贪婪汤排序、贪婪汤随机和均匀汤)并重复了作者的结果。然后,我们介绍了一种新的汤 recipe,称为削切汤。从汤中得出的结果比预训练视觉Transformer中的最优个体模型还要好,但对于ResNet和EfficientNet却表现得很糟糕。我们的削切汤表现比原始论文中的均匀和贪婪汤更好。我们还讨论了在实验中发现的权重平均限制。我们的模型汤库和与不同模型的实验代码可在这里找到:这个https URL。

URL

https://arxiv.org/abs/2301.10092

PDF

https://arxiv.org/pdf/2301.10092.pdf


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