Theoretical Guarantees of Data Augmented Last Layer Retraining Methods

• URL: http://arxiv.org/abs/2405.05934v1
• Date: Thu, 9 May 2024 17:16:54 GMT
• Title: Theoretical Guarantees of Data Augmented Last Layer Retraining Methods
• Authors: Monica Welfert, Nathan Stromberg, Lalitha Sankar,
• Abstract summary: Linear last layer retraining strategies have been shown to achieve state-of-the-art performance for worst-group accuracy. We present the optimal worst-group accuracy when modeling the distribution of the latent representations. We evaluate and verify our results for both synthetic and large publicly available datasets.
• Score: 5.352699766206809
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ensuring fair predictions across many distinct subpopulations in the training data can be prohibitive for large models. Recently, simple linear last layer retraining strategies, in combination with data augmentation methods such as upweighting, downsampling and mixup, have been shown to achieve state-of-the-art performance for worst-group accuracy, which quantifies accuracy for the least prevalent subpopulation. For linear last layer retraining and the abovementioned augmentations, we present the optimal worst-group accuracy when modeling the distribution of the latent representations (input to the last layer) as Gaussian for each subpopulation. We evaluate and verify our results for both synthetic and large publicly available datasets.

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