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AdapterTune: Zero-Initialized Low-Rank Adapters for Frozen Vision Transformers
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- Proof freshness
- stale
- Proof status
- unverified
- Display score
- 9/10
- Last proof check
- 2026-03-19
- Score updated
- 2026-04-02
- Score fresh until
- 2026-05-02
- References
- 0
- Source count
- 0
- Coverage
- 50%
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AdapterTune: Zero-Initialized Low-Rank Adapters for Frozen Vision Transformers
Canonical ID adaptertune-zero-initialized-low-rank-adapters-for-frozen-vision-transformers | Route /signal-canvas/adaptertune-zero-initialized-low-rank-adapters-for-frozen-vision-transformers
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Dimensions overall score 9.0
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Claim map
- Evidencepartial
AdapterTune improves top-1 accuracy over head-only transfer by +14.9 points on average
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- Evidencepartial
training only 0.92 of the parameters required by full fine-tuning
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- Evidencepartial
outperforms full fine-tuning on 10 of 15 dataset-backbone pairs
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- Evidencepartial
whose up-projection is zero-initialized, guaranteeing that the adapted network starts exactly at the pretrained function and eliminates early-epoch representation drift
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- Evidencepartial
The resulting excess-risk decomposition predicts monotonic but diminishing accuracy gains with increasing rank, an "elbow" behavior we confirm through controlled sweeps
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- Evidencepartial
Across the full benchmark, AdapterTune improves over head-only transfer on every dataset-backbone pair tested
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- Evidencepartial
Frozen-backbone transfer with Vision Transformers faces two under-addressed issues: optimization instability when adapters are naively inserted into a fixed feature extractor
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- Evidencepartial
we formalize adapter rank as a capacity budget for approximating downstream task shifts in feature space
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- Evidencepartial
which augments each transformer block with a residual low-rank bottleneck whose up-projection is zero-initialized, guaranteeing that the adapted network starts exactly at the pretrained function and eliminates early-epoch representation drift.
ImplicationpartialThis is a core technical innovation explicitly stated in the abstract and abstract.
Verificationpartialpartial
- Evidencepartial
On a core 5 dataset transfer suite, AdapterTune improves top-1 accuracy over head-only transfer by +14.9 points on average
ImplicationpartialSpecific numerical result reported in the abstract with clear comparison.
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- Evidencepartial
while training only 0.92 of the parameters required by full fine-tuning
ImplicationpartialSpecific numerical result reported in the abstract, highlighting efficiency.
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- Evidencepartial
and outperforms full fine-tuning on 10 of 15 dataset-backbone pairs.
ImplicationpartialSpecific numerical result reported in the abstract, indicating superiority over a strong baseline.
Verificationpartialpartial
