How self-supervised learning works: a masked autoencoder finds brain lesions with zero labels and beats training from scratch with few labels
In-domain masked-volume self-supervised pretraining for label-efficient whole-brain MRI parcellation. 3D U-Net on OASIS-3, tested with 10, 25, 50 and 104 labeled scans under 5-fold cross-validation (MONAI, PyTorch).
How semi-supervised learning works: FixMatch on brain-MRI-style scans, why it fails on subtle lesions, and how to fix it
Active-learning label-efficiency study on the VarWISE NEOWISE infrared variable catalog: 86% fewer labels for equal accuracy, with the gain concentrated almost entirely in the rarest variability classes. Includes an independent SIMBAD validation of VarWISE's published classifications.
PyTorch pipeline for few-shot semantic segmentation of 3D seismic volumes, with configurable data partitioning and support selection.
Label-efficiency benchmark — margin sampling hits target accuracy with 38% fewer labels than random, while least-confidence/entropy underperform it. The honest 'which strategy, not whether'
A computer vision pipeline exploring self-supervised learning (SSL) for label-efficient outdoor waste detection and multi-object tracking using YOLO and ByteTrack.
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