Cross-Modal Knowledge Distillation without Paired Data: Theoretical Foundation and Algorithm
arXiv:2606.10504v1 Announce Type: new Abstract: Cross-modal knowledge distillation (CMKD) studies how a (large) teacher model trained on one type of data (e.g., images) can guide a (smaller) student model building on another type of data (e.g., text/audio). Existing CMKD methods often require paired multi-modal data with aligned semantics, but obtaining such paired data are often costly and impractical. To mitigate this limitation, we develop a new CMKD framework for the more challenging setting where paired data are unavailable. In particular, we establish a cross-modal distributional relatio
The paper addresses a significant practical limitation in existing cross-modal knowledge distillation methods, which often rely on costly paired multi-modal data.
This breakthrough could democratize advanced AI model development by making cross-modal learning accessible with more readily available unpaired data, boosting efficiency and reducing resource demands.
AI models can now learn across different data types (e.g., images and text) without requiring meticulously paired datasets, accelerating multimodal AI development and deployment.
- · AI developers
- · Small and medium AI companies
- · Multimodal AI applications
- · Data-scarce domains
- · Companies specializing solely in paired data collection
- · Resource-intensive AI training approaches
More sophisticated multimodal AI models become easier and cheaper to create, expanding their applicability.
This could lead to a proliferation of AI agents that can seamlessly process and generate information across various modalities.
Reduced data dependency might decentralize AI development, lessening the advantage of those with vast proprietary paired datasets.
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Read at arXiv cs.AI