  Data samples generated by several real world processes are dynamic in nature
\textit{i.e.}, their characteristics vary with time. Thus it is not possible to
train and tackle all possible distributional shifts between training and
inference, using the host of transfer learning methods in literature. In this
paper, we tackle this problem of adapting to domain shift at inference time
\textit{i.e.}, we do not change the training process, but quickly adapt the
model at test-time to handle any domain shift. For this, we propose to enforce
consistency of predictions of data sampled in the vicinity of test sample on
the image manifold. On a host of test scenarios like dealing with corruptions
(CIFAR-10-C and CIFAR-100-C), and domain adaptation (VisDA-C), our method is at
par or significantly outperforms previous methods.
