Title: Tiny Adapters for Vision Transformers. Abstract: Vision Transformers (ViTs) have become one of the dominant architectures in computer vision and pretrained ViT models are commonly adapted to new tasks via fine-tuning of its  parameters. Recent works in NLP proposed a variety of parameter-efficient transfer learning methods such as adapters to avoid the prohibitive storage cost of fine-tuning. 

In this work, we start from the observation that adapters perform poorly when the dimension of adapters is small and we propose a training algorithm that addresses this issue. We start from large adapters which can be trained easily and iteratively reduce the size of every adapter. We introduce a scoring function that can compare neuron importance across layers and consequently allow automatic estimation of the hidden dimension of every adapter. Our method outperforms existing approaches in terms of the trade-off between accuracy and trained parameters across domain adaptation benchmarks. We will release our code publicly upon acceptance.