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About this paper
Suraj Yadav
An adaptation framework for visual foundation models, Self-Routed Tensor Adapters (SRTA), intrinsically computes routing decisions from its low-rank Tucker-decomposed representation for multi-domain learning. This approach achieves comparable or slightly better accuracy (88.1% average) across five benchmarks, while requiring 3.4 to 4.8 times fewer parameters than state-of-the-art Mixture-of-Experts-based methods.
Key insights
- Routing for multi-domain adaptation can be intrinsically derived from the adapter's low-rank representation, removing the need for external gating networks.
- Tucker decomposition can be utilized beyond mere compression to create a dynamic adaptation space that blends shared and domain-specific factors.
- Providing progressive depth-weighted supervision to routing decisions across adapter layers improves the learning of domain-aware pathways, particularly in complex multi-domain scenarios.
Summary by alphaXiv.
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