data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language (2202.03555v3)

Abstract: 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.

• The paper introduces a unified framework that predicts latent representations for speech, vision, and language using self-distillation with Transformer models.
• It employs modality-specific masking strategies, achieving high accuracy on benchmarks like ImageNet, Librispeech, and GLUE.
• The results demonstrate state-of-the-art performance, providing a practical approach for cross-modal learning and reducing preprocessing complexity.

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

The paper entitled "data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language" introduces a unified self-supervised learning framework that can be effectively applied to multiple modalities, specifically speech, vision, and language. This work moves towards a general approach in self-supervised learning by using a consistent learning method across different modalities while retaining modality-specific feature extractors and masking strategies.

Core Methodology

The central innovation of this research lies in the use of contextualized target representations. Unlike existing methods that predict local targets such as words in NLP, visual tokens in vision, or frames in speech, data2vec predicts latent representations derived from masked input data of the entire context. This is achieved using a self-distillation setup with Transformer models, where the same model serves as both teacher and student. The key steps include:

1. Teacher Encoding: The full input data is encoded to produce target representations using an exponentially moving average (EMA) of the student's parameters.
2. Student Prediction: The same model, in student mode, predicts the latent representations of the masked input.
3. Aggregation of Targets: Contextualized targets are obtained by averaging the representations over multiple layers of the teacher model, enhancing robustness and informativeness.

The masking strategies are tailored according to the data modality: block-wise masking for vision, span masking for speech, and token masking for language.

Empirical Results

The effectiveness of data2vec is demonstrated across major benchmarks in speech recognition, image classification, and natural language understanding:

1. Image Classification on ImageNet-1K:
   • The ViT-B model of data2vec achieved a top-1 validation accuracy of 84.2%, surpassing other single-model approaches.
   • The ViT-L model recorded a top-1 accuracy of 86.6%, outperforming existing methods, including BEiT and SimMIM.
2. Speech Recognition:
   • On the Librispeech dataset, data2vec demonstrated superior performance in low-resource setups, achieving word error rates (WER) of 12.3% with 10 minutes of labeled data, significantly better than the alternatives like wav2vec 2.0 and HuBERT.
   • For the large model using 60K hours of pre-training data, data2vec achieved a WER of 3.7% on the full 960 hours of labeled data.
3. Natural Language Processing:
   • On the GLUE benchmark, data2vec outperformed the re-trained RoBERTa baseline with an average score improvement. Notably, it achieved a considerable increase in Matthews correlation on the CoLA task, highlighting its effectiveness in grammatical acceptability detection.

Discussion

Implications

Practical Implications:

• Data2vec demonstrates that a single algorithm can effectively perform across various modalities, simplifying the process of multi-modal learning.
• The approach reduces the need for modality-specific pre-processing steps, potentially streamlining development pipelines in real-world applications.

Theoretical Implications:

• This research supports the prospect that general learning mechanisms can be applicable across diverse data types. This aligns with theories in cognitive science suggesting that humans utilize similar neural processes across different sensory inputs.

Future Directions

The promising results of data2vec open several potential avenues for future research:

1. Unified Modalities: Future work might explore extending data2vec to train on multiple modalities simultaneously, enriching cross-modal representation learning.
2. Alternative Architectures: Experimentation with non-Transformer architectures could shed light on the broader applicability of the data2vec principles.
3. Enhanced Masking Strategies: Further optimization of masking techniques for each modality may yield additional performance gains.

Conclusion

The paper presents a compelling case for a general framework in self-supervised learning, showing competitive or superior performance across speech, vision, and natural language tasks. By leveraging contextualized target representations and a consistent learning strategy, data2vec effectively demonstrates that a unified approach can be both practical and powerful. This work lays the groundwork for future developments in multi-modal learning and the continued convergence of machine learning methodologies across various domains.