Unfreezing the patch embedding during low-rank adaptation of a vision transformer yields 96.35% accuracy on encrypted CIFAR-10 with 0.71M trainable parameters.
Melo: Low-rank adaptation is better than fine-tuning for medical image diagnosis,
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Effective Fine-Tuning of Vision Transformers with Low-Rank Adaptation for Privacy-Preserving Image Classification
Unfreezing the patch embedding during low-rank adaptation of a vision transformer yields 96.35% accuracy on encrypted CIFAR-10 with 0.71M trainable parameters.