Title: BayesAdapter: Being Bayesian, Inexpensively and Robustly, via Bayesian Fine-tuning. Abstract: Despite their theoretical appealingness, Bayesian neural networks (BNNs) are falling far behind in terms of adoption in real-world applications compared with normal NNs, mainly due to their limited scalability in training, and low fidelity in their uncertainty estimates. In this work, we develop a new framework, named BayesAdapter, to address these issues and bring Bayesian deep learning to the masses. The core notion of BayesAdapter is to adapt pre-trained deterministic NNs to be BNNs via Bayesian fine-tuning. We implement Bayesian fine-tuning with a plug-and-play instantiation of stochastic variational inference, and propose exemplar reparameterization to reduce gradient variance and stabilize the fine-tuning. Together, they enable training BNNs as if one were training deterministic NNs with minimal added overheads. During Bayesian fine-tuning, we further propose an uncertainty regularization to supervise and calibrate the uncertainty quantification of learned BNNs at low cost. To empirically evaluate BayesAdapter, we conduct extensive experiments on a diverse set of challenging benchmarks, and observe satisfactory training efficiency, competitive predictive performance, and calibrated and faithful uncertainty estimates. 