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AdapterTune: Zero-Initialized Low-Rank Adapters for Frozen Vision Transformers
🤖AI Summary
AdapterTune introduces a new method for efficiently fine-tuning Vision Transformers by using zero-initialized low-rank adapters that start at the pretrained function to prevent optimization instability. The technique achieves +14.9 point accuracy improvement over head-only transfer while using only 0.92% of parameters needed for full fine-tuning.
Key Takeaways
- →AdapterTune solves optimization instability in Vision Transformer transfer learning through zero-initialized low-rank bottlenecks.
- →The method provides principled guidance for setting adapter capacity using formal rank analysis as a budget for approximating task shifts.
- →Testing across 9 datasets and 3 backbone scales shows consistent improvements over head-only transfer methods.
- →AdapterTune outperformed full fine-tuning on 10 of 15 dataset-backbone pairs while using significantly fewer parameters.
- →The approach demonstrates monotonic but diminishing accuracy gains with increasing rank, confirming theoretical predictions.
#vision-transformers#transfer-learning#adapter-tuning#low-rank-adaptation#parameter-efficiency#computer-vision#machine-learning#optimization
Read Original →via arXiv – CS AI
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