[Paper] Implicit Data Synthesis for Contrastive Unsupervised Data Augmentation

Source: arXiv - 2606.07498v1

Overview

Scientific observations generate large quantities of unlabeled data which is laborious to hand-label, making unsupervised learning techniques valuable for processing datasets. Among these approaches, contrastive learning provides a convenient mechanism for extracting structural representations from unannotated datasets. For natural imagery, the general approach is to use a variety of data-space augmentation methods in order to generate synthetic samples; however, for scientific observations data-space perturbations can fundamentally alter the underlying data. Our proposed method is to generate contrastive samples by perturbing the network weights rather than the underlying data, thus more closely preserving the structure of the data. We demonstrate this technique using a SimCLR-based pipeline applied over radar observations of meteors, and show performance gains under matched protocols.

Key Contributions

This paper presents research in the following areas:

• cs.CV

Methodology

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Practical Implications

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Authors

• Patrick Kage
• Trevor Hedges
• N. Siddharth
• Pavlos Andreadis

Paper Information

• arXiv ID: 2606.07498v1
• Categories: cs.CV
• Published: June 5, 2026
• PDF: Download PDF