Related papers: Anchor Data Augmentation

Anchor Data Augmentation

URL: http://arxiv.org/abs/2311.06965v2
Date: Mon, 27 Nov 2023 19:22:27 GMT
Title: Anchor Data Augmentation
Authors: Nora Schneider, Shirin Goshtasbpour, Fernando Perez-Cruz
Abstract summary: We propose a novel algorithm for data augmentation in nonlinear over-parametrized regression. Our data augmentation algorithm borrows from the literature on causality and extends the recently proposed Anchor regression (AR) method for data augmentation.
Score: 53.39044919864444
License: http://creativecommons.org/publicdomain/zero/1.0/
Abstract: We propose a novel algorithm for data augmentation in nonlinear over-parametrized regression. Our data augmentation algorithm borrows from the literature on causality and extends the recently proposed Anchor regression (AR) method for data augmentation, which is in contrast to the current state-of-the-art domain-agnostic solutions that rely on the Mixup literature. Our Anchor Data Augmentation (ADA) uses several replicas of the modified samples in AR to provide more training examples, leading to more robust regression predictions. We apply ADA to linear and nonlinear regression problems using neural networks. ADA is competitive with state-of-the-art C-Mixup solutions.

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