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Anchor Data Augmentation (ADA): A Domain-Agnostic Method for Enhancing Regression Modelsby@anchoring

Anchor Data Augmentation (ADA): A Domain-Agnostic Method for Enhancing Regression Models

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November 14th, 2024
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Anchor Data Augmentation (ADA) introduces domain-agnostic data augmentation for regression tasks by clustering data and augmenting samples based on the center of each cluster. ADA improves generalization without requiring expensive generative models and is computationally efficient, with minimal performance loss even if it doesn’t improve results.
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Academic Research Paper

Academic Research Paper

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

Authors:

(1) Nora Schneider, Computer Science Department, ETH Zurich, Zurich, Switzerland (nschneide@student.ethz.ch);

(2) Shirin Goshtasbpour, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (shirin.goshtasbpour@inf.ethz.ch);

(3) Fernando Perez-Cruz, Computer Science Department, ETH Zurich, Zurich, Switzerland and Swiss Data Science Center, Zurich, Switzerland (fernando.perezcruz@sdsc.ethz.ch).

Abstract and 1 Introduction

2 Background

2.1 Data Augmentation

2.2 Anchor Regression

3 Anchor Data Augmentation

3.1 Comparison to C-Mixup and 3.2 Preserving nonlinear data structure

3.3 Algorithm

4 Experiments and 4.1 Linear synthetic data

4.2 Housing nonlinear regression

4.3 In-distribution Generalization

4.4 Out-of-distribution Robustness

5 Conclusion, Broader Impact, and References


A Additional information for Anchor Data Augmentation

B Experiments

3 Anchor Data Augmentation

In this section, we introduce Anchor Data Augmentation (ADA), a domain-independent data augmentation method inspired by AR. ADA does not require previous knowledge about the data invariances nor manually engineered transformations. As opposed to existing domain-agnostic data augmentation methods [10, 45, 46], we do not require training of an expensive generative model, and the augmentation only adds marginally to the computation complexity of the training. In addition, since ADA originates from a causal regression problem, it can be readily applied to regression problems. Even when ADA does not improve performance, its effect on performance remains minimal.


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Figure 1: Comparison of ADA augmentations on a nonlinear Cosine data model. For a larger partition size, ADA augmentations are more accurate due to the high local variability of the Cosine function. We used k-means clustering to construct A and γ ∈ {1/2, 2/3, 1.03/2, 2.0}.

Figure 1: Comparison of ADA augmentations on a nonlinear Cosine data model. For a larger partition size, ADA augmentations are more accurate due to the high local variability of the Cosine function. We used k-means clustering to construct A and γ ∈ {1/2, 2/3, 1.03/2, 2.0}.


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This paper is available on arxiv under CC0 1.0 DEED license.


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