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

While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture. Instead of predicting modality-specific targets such as words, visual tokens or units of human speech which are local in nature, data2vec predicts contextualized latent representations that contain information from the entire input. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance to predominant approaches.

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Results from the Paper

Ranked #9 on Speech Recognition on LibriSpeech test-other (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Linguistic Acceptability CoLA data2vec Accuracy 60.3% # 23
Image Classification ImageNet data2vec (ViT-H) Top 1 Accuracy 86.6% # 128
Number of params 656M # 918
Speech Recognition LibriSpeech test-other data2vec Word Error Rate (WER) 3.7 # 9
Natural Language Inference QNLI data2vec Accuracy 91.1% # 27
Natural Language Inference RTE data2vec Accuracy 69.9% # 35