NuWa: Deriving Lightweight Class-Specific Vision Transformers for Edge Devices

NuWa is a novel method for deriving lightweight class-specific Vision Transformers (ViTs) tailored for resource-constrained edge devices, addressing the limitations of existing model compression techniques. It employs self-knowledge purification to eliminate class-detrimental weights and utilizes closed-form optimization to create compact ViTs without the need for post-pruning retraining. Experimental results indicate that NuWa achieves up to 29% higher accuracy on class-specific tasks compared to state-of-the-art training-free pruning methods, with a 33.69x speedup in pruning and a 99.83% reduction in pruning costs, making it highly efficient for practitioners focused on deploying optimized models in edge environments.