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• AdapterTune improves top-1 accuracy over head-only transfer by +14.9 points on average on a core 5 dataset transfer suite.result 95%
• AdapterTune trains only 0.92% of the parameters required by full fine-tuning.technical 95%
• AdapterTune outperforms full fine-tuning on 10 of 15 dataset-backbone pairs.result 95%
• AdapterTune's zero-initialized up-projection guarantees the adapted network starts exactly at the pretrained function and eliminates early-epoch representation drift.method 90%
• The excess-risk decomposition predicts monotonic but diminishing accuracy gains with increasing adapter rank, an 'elbow' behavior confirmed through controlled sweeps.technical 85%
• AdapterTune improves over head-only transfer on every dataset-backbone pair tested across the full benchmark.result 95%
• Frozen-backbone transfer with Vision Transformers faces optimization instability when adapters are naively inserted into a fixed feature extractor.limitation 85%
• The paper formalizes adapter rank as a capacity budget for approximating downstream task shifts in feature space.method 90%
• AdapterTune guarantees that the adapted network starts exactly at the pretrained function by zero-initializing the up-projection of residual low-rank bottlenecks.technical 95%
• AdapterTune improves top-1 accuracy by +14.9 points on average over head-only transfer on a core 5 dataset transfer suite.result 95%
• AdapterTune trains only 0.92 of the parameters required by full fine-tuning while achieving improved accuracy.result 95%
• AdapterTune outperforms full fine-tuning on 10 out of 15 dataset-backbone pairs.result 90%

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