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Anchor Data Augmentation as a Generalized Variant of C-Mixupby@anchoring

Anchor Data Augmentation as a Generalized Variant of C-Mixup

by Anchoring
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November 14th, 2024
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ADA generalizes C-Mixup by mixing multiple samples based on their cluster membership, preserving nonlinear relationships in augmented regression data. The approach allows augmentations to stay within or extend beyond the convex hull of original samples, improving data diversity while maintaining model accuracy.
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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

3.1 Comparison to C-Mixup

image

3.2 Preserving nonlinear data structure

image


The AR modification Equations 5 and 6 do not preserve the nonlinear relation between the target and predictors,


image


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


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Anchoring provides a steady start, grounding decisions and perspectives in clarity and confidence.

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