In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning — mentioned by blackbear_

Sat, 22 Apr 2023 22:04:10 GMT

> Because so far we've never seen an AI improve based on its own output. Maybe it's because AI is such an overloaded term, but this is pretty commonplace for (semi-)supervised learning algorithms. Pseudo-labeling [1,2] is an example of this that has been around for decades. When done properly it does improve the performance of the original model, up to a certain limit (far from the singularity). Moreover, it is apparently possible to improve a model's performance by augmenting it's training set with synthetic examples generated by a second model [3]. Finally, boosting [4] can also be seen as…

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In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning · HackerPapers discussions

In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning — mentioned by blackbear_