mlsanityData to 14 Oct 2026

FAAGC: Feature Augmentation on Adaptive Geodesic Curve Based on the shape space theory

arXiv 2501.18619 ↗First posted 25 Jan 2025cs.CV cs.LGcited by 0

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Abstract (arXiv)

Deep learning models have been widely applied across various domains and industries. However, many fields still face challenges due to limited and insufficient data. This paper proposes a Feature Augmentation on Adaptive Geodesic Curve (FAAGC) method in the pre-shape space to increase data. In the pre-shape space, objects with identical shapes lie on a great circle. Thus, we project deep model representations into the pre-shape space and construct a geodesic curve, i.e., an arc of a great circle, for each class. Feature augmentation is then performed by sampling along these geodesic paths. Extensive experiments demonstrate that FAAGC improves classification accuracy under data-scarce conditions and generalizes well across various feature types.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 35 references that are in the map. First posted on arXiv 25 Jan 2025.

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References · 19

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7 more
  1. 2018Synthetic Data Augmentation using GAN for Improved Liver Lesion Classificationcited by 136
  2. 2020Feature Space Augmentation for Long-Tailed Datacited by 136
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  5. 2021Data Augmentation with Variational Autoencoders and Manifold Samplingcited by 16
  6. 2021GLASS: Geometric Latent Augmentation for Shape Spacescited by 16
  7. 2022Domain-guided data augmentation for deep learning on medical imagingcited by 5

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