Faced with new and different data during testing, a model must adapt itself. We consider the setting of fully test-time adaptation, in which a supervised model confronts unlabeled test data from a different distribution, without the help of its labeled training data. We propose an entropy minimization approach for adaptation: we take the model’s confidence as our objective as measured by the entropy of its predictions. During testing, we adapt the model by modulating its representation with affine transformations to minimize entropy. Our experiments show improved robustness to corruptions for image classification on CIFAR-10⁄100 and ILSVRC and demonstrate the feasibility of target-only domain adaptation for digit classification on MNIST and SVHN.