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pseudo-label-selection

topic1 events
papersTODAY 04:00 UTC

Geometry-Based Pseudo-Label Selection Proposed for Cold-Start Semi-Supervised Learning

A new arXiv paper examines semi-supervised learning in the cold-start setting, where only a handful of labeled examples are available. The authors argue that standard methods, which let a classifier pick its own pseudo-labels by confidence, break down in this regime because the model is unreliable early on. Their approach instead selects pseudo-labels using the geometry of the data rather than the model's confidence, decoupling label selection from classifier training.

semi-supervised-learning/pseudo-labeling/arxiv/cold-start