ICPL generates pseudo-labels by clustering embeddings with KMeans, keeps only confident ones, and uses them to train class-incremental models without human labels, losing about 10 points versus supervised CIL but beating class-iNCD baselines by more than 5.
Class-weighted classification: Trade-offs and robust approaches
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Unsupervised Incremental Learning Using Confidence-Based Pseudo-Labels
ICPL generates pseudo-labels by clustering embeddings with KMeans, keeps only confident ones, and uses them to train class-incremental models without human labels, losing about 10 points versus supervised CIL but beating class-iNCD baselines by more than 5.