Review - data2vec: A General Framework for Self-supervised Learning in Speech, Vision, and Language

The paper "data2vec: A General Framework for Self-supervised Learning in Speech, Vision, and Language" presents a novel SSL framework that applies the same learning method to speech, NLP, or computer vision, achieving state-of-the-art results. Unlike previous methods, data2vec predicts contextualized latent representations rather than modality-specific targets. It uses a teacher network to compute target representations and a student network to predict them from a masked view of the input. This approach simplifies training models by focusing on their own representations regardless of the modality. Data2vec has shown promising results in speech processing tasks, outperforming other state-of-the-art SSL methods.

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