3 papers
cs.LG2026
Learning by Shifting: Temporal View Construction for Time Series Contrastive Learning
Abdul-Kazeem Shamba, Kerstin Bach, Gavin Taylor
Supervised learning demands large quantities of labeled data, a bottleneck that is expensive and reliant on domain-specific expertise. Self-supervised learning, particularly contra…
cs.LG2025
eMargin: Revisiting Contrastive Learning with Margin-Based Separation
Abdul-Kazeem Shamba, Kerstin Bach, Gavin Taylor
We revisit previous contrastive learning frameworks to investigate the effect of introducing an adaptive margin into the contrastive loss function for time series representation le…
cs.CV2025
Towards Generating Realistic Underwater Images
Abdul-Kazeem Shamba
This paper explores the use of contrastive learning and generative adversarial networks for generating realistic underwater images from synthetic images with uniform lighting. We i…