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Continuous Pseudo-Labeling from the Start



Self-training (ST), or pseudo-labeling has sparked significant interest in the automatic speech recognition (ASR) community recently because of its success in harnessing unlabeled data. Unlike prior semi-supervised learning approaches that relied on iteratively regenerating pseudo-labels (PLs) from a trained model and using them to train a new model, recent state-of-the-art methods perform ‘continuous training’ where PLs are generated using a very recent version of the model being trained. Nevertheless, these approaches still rely on bootstrapping the ST using an initial supervised learning…



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Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials

In practice, training using federated learning can be orders of magnitude slower than standard centralized training. This severely limits the amount of experimentation and tuning that can be done, making it challenging to obtain good performance on a...

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024 Article Source link and Credit

Introducing Apple’s On-Device and Server Foundation Models

Introducing Apple’s On-Device and Server Foundation Models Article Source link and Credit