Title: KITE: A Kernel-based Improved Transferability Estimation Method. Abstract: Transferability estimation has emerged as an important problem in transfer learning. A transferability estimation method takes as inputs a set of pre-trained models and decides which pre-trained model can deliver the best transfer learning performance. Existing methods tackle this problem by analyzing the output of the pre-trained model or by comparing the pre-trained model with a probe model trained on the target dataset. However, neither is sufficient to provide reliable and efficient transferability estimations. In this paper, we present a novel perspective and introduce \textsc{Kite}, as a \underline{K}ernel-based \underline{I}mproved \underline{T}ransferability \underline{E}stimation method. \textsc{Kite} is based on the key observations that the separability of the pre-trained features and the similarity of the pre-trained features to random features are two important factors for estimating transferability. Inspired by kernel methods, \textsc{Kite} adopts \emph{centered kernel alignment} as an effective way to assess feature separability and feature similarity. \textsc{Kite} is easy to interpret, fast to compute, and robust to the target dataset size. We evaluate the performance of \textsc{Kite} on a recently introduced large-scale model selection benchmark. The benchmark contains 8 source dataset, 6 target datasets and 4 architectures with a total of 32 pre-trained models. Extensive results show that \textsc{Kite} outperforms existing methods by a large margin for transferability estimation.