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Found 2 papers, 1 papers with code
How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning
We conduct experiments with SSL and AL on simulated data challenges and find that random sampling does not mitigate confirmation bias and, in some cases, leads to worse performance than supervised learning.
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification
Node classification is one of the core tasks on attributed graphs, but successful graph learning solutions require sufficiently labeled data.
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