TinyDrop uses a lightweight model's confidence and attention map to early-exit easy samples and drop uninformative tokens in frozen ViTs, cutting FLOPs by up to 87%.
Un- like convolutional networks, ViTs process all input tokens uniformly with identical computational cost, resulting in quadratic complexity relative to token count
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TinyDrop: Tiny Model Guided Token Dropping for Vision Transformers
TinyDrop uses a lightweight model's confidence and attention map to early-exit easy samples and drop uninformative tokens in frozen ViTs, cutting FLOPs by up to 87%.