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Continual Semi-Supervised Learning through Contrastive Interpolation Consistency. (arXiv:2108.06552v3 [stat.ML] UPDATED)
Aug. 30, 2022, 1:12 a.m. | Matteo Boschini, Pietro Buzzega, Lorenzo Bonicelli, Angelo Porrello, Simone Calderara
stat.ML updates on arXiv.org arxiv.org
Continual Learning (CL) investigates how to train Deep Networks on a stream
of tasks without incurring forgetting. CL settings proposed in literature
assume that every incoming example is paired with ground-truth annotations.
However, this clashes with many real-world applications: gathering labeled
data, which is in itself tedious and expensive, becomes infeasible when data
flow as a stream. This work explores Continual Semi-Supervised Learning (CSSL):
here, only a small fraction of labeled input examples are shown to the learner.
We assess …
arxiv continual learning semi-supervised semi-supervised learning supervised learning
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