Astra-CLR is a multi-filter time-series Transformer pre-trained via contrastive learning on 2.1 million ZTF light curves that achieves 0.70 accuracy classifying 12 variability classes, rising to 0.77 with partial fine-tuning.
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Localization uncertainty visualization in AI predictions improves human annotation quality and speed by redirecting effort toward high-uncertainty boxes.
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Multi-Scale Contrastive Attention for Light-Curve Representation Learning
Astra-CLR is a multi-filter time-series Transformer pre-trained via contrastive learning on 2.1 million ZTF light curves that achieves 0.70 accuracy classifying 12 variability classes, rising to 0.77 with partial fine-tuning.
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From Model Uncertainty to Human Attention: Localization-Aware Visual Cues for Scalable Annotation Review
Localization uncertainty visualization in AI predictions improves human annotation quality and speed by redirecting effort toward high-uncertainty boxes.