Papers › data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | ImageNet | data2vec (ViT-H) | Number of params | 656M | #135 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | data2vec (ViT-H) | Top 1 Accuracy | 86.6% | #135 of 1060 | Archive leaderboard | report |
| Linguistic Acceptability | CoLA | data2vec | Accuracy | 60.3% | #30 of 43 | Archive leaderboard | report |
| Natural Language Inference | QNLI | data2vec | Accuracy | 91.1% | #30 of 43 | Archive leaderboard | report |
| Natural Language Inference | RTE | data2vec | Accuracy | 69.9% | #55 of 90 | Archive leaderboard | report |
| Paraphrase Identification | Quora Question Pairs | data2vec | Accuracy | 92.4 | #16 of 31 | Archive leaderboard | report |
| Speech Recognition | LibriSpeech test-other | data2vec | Word Error Rate (WER) | 3.7 | #12 of 53 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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