arXiv:2606.10938v1 Announce Type: new Abstract: Trajectory data augmentation is a promising approach to mitigate data scarcity in machine learning applications, but its utility has been limited by the complexity of preserving spatio-temporal coherence. Although prior work demonstrated the viability of geometric perturbation, it relied on naive random selection, leaving a critical gap in understanding which trajectories should be augmented for maximal benefit. This thesis addresses this gap by developing a systematic and scalable framework to evaluate five systematic selection strategies: Outli
The accelerating demand for robust machine learning models across diverse applications necessitates more efficient and effective data augmentation techniques, moving beyond simplistic random approaches.
This development addresses a critical limitation in AI model training, potentially leading to more accurate and reliable AI systems even with limited datasets, impacting industries reliant on data-driven intelligence.
The focus shifts from general data augmentation to strategic, systematic selection of trajectories, optimizing resource allocation and improving model performance with higher confidence.
- · Machine Learning Developers
- · AI-reliant Industries
- · Autonomous Systems
- · Random Data Augmentation Methods
Improved performance and robustness of AI models, particularly in data-scarce domains.
Faster development cycles and reduced computational costs for training AI systems as data efficiency improves.
Broader adoption of AI in fields currently constrained by limited, high-quality data, accelerating various automation initiatives.
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Read at arXiv cs.LG