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ViT-Linearizer: Distilling Quadratic Knowledge into Linear-Time Vision Models
🤖AI Summary
Researchers developed ViT-Linearizer, a distillation framework that transfers Vision Transformer knowledge into linear-time models, addressing quadratic complexity issues for high-resolution inputs. The method achieves 84.3% ImageNet accuracy while providing significant speedups, bridging the gap between efficient RNN-based architectures and transformer performance.
Key Takeaways
- →ViT-Linearizer transfers quadratic Vision Transformer knowledge into linear-time recurrent models through cross-architecture distillation.
- →The framework uses activation matching and masked prediction to maintain performance while reducing computational complexity.
- →Method achieves 84.3% top-1 accuracy on ImageNet with a base-sized model, competitive with traditional transformers.
- →Approach provides notable speedups for high-resolution tasks, addressing hardware inference challenges.
- →Results demonstrate potential for RNN-based solutions in large-scale visual tasks as alternatives to transformers.
#vision-transformers#model-distillation#linear-complexity#mamba-architecture#computer-vision#efficiency#imagenet#rnn-models#inference-optimization
Read Original →via arXiv – CS AI
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