Seq-UPS: Sequential Uncertainty-aware Pseudo-label Selection for Semi-Supervised Text Recognition. (arXiv:2209.00641v1 [cs.CV])

This paper looks at semi-supervised learning (SSL) for image-based text
recognition. One of the most popular SSL approaches is pseudo-labeling (PL). PL
approaches assign labels to unlabeled data before re-training the model with a
combination of labeled and pseudo-labeled data. However, PL methods are
severely degraded by noise and are prone to over-fitting to noisy labels, due
to the inclusion of erroneous high confidence pseudo-labels generated from
poorly calibrated models, thus, rendering threshold-based selection
ineffective. Moreover, the combinatorial complexity of …

arxiv semi-supervised text uncertainty ups