What is recorded here?
Regressed contextual latent representations from an EMA teacher's full input using masked student views, applying one self-distillation objective across separate speech, image and text settings. data2vec2 skipped masked inputs, used a convolutional decoder and amortized teacher computation across several masked student views.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Inherits EMA teachers, BYOL/DINO-style self-distillation and masking. Same learning method across modalities is not a single jointly trained multimodal model: feature encoders and masking remain modality specific. Not invention of all latent prediction or evidence for a biological learning theory; benchmark gains and anti-collapse behavior have configuration limits. Version2 inherits visible-only encoding and multi-mask reuse; reported16.4fold image pretraining reduction is protocol-specific, not every modality or end-to-end workflow.
Read the original work
- data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
- Efficient Self-supervised Learning with Contextualized Target Representations for Vision, Speech and Language
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Alexei Baevski; Wei-Ning Hsu; Qiantong Xu; Arun Babu; Jiatao Gu; Michael Auli
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
data2vec family: contextual targets and efficient masked self-distillation
Regressed contextual latent representations from an EMA teacher's full input using masked student views, applying one self-distillation objective across separate speech, image and text settings. data2vec2 skipped masked inputs, used a convolutional decoder and amortized teacher computation across several masked student views.
Scientific scope
Inherits EMA teachers, BYOL/DINO-style self-distillation and masking. Same learning method across modalities is not a single jointly trained multimodal model: feature encoders and masking remain modality specific. Not invention of all latent prediction or evidence for a biological learning theory; benchmark gains and anti-collapse behavior have configuration limits. Version2 inherits visible-only encoding and multi-mask reuse; reported16.4fold image pretraining reduction is protocol-specific, not every modality or end-to-end workflow.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Alexei Baevski; Wei-Ning Hsu; Qiantong Xu; Arun Babu; Jiatao Gu; Michael Auli
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.