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'
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Updated
Jul 25, 2026 - Python
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.
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.
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