Convolutional neural networks (CNNs) achieve state-of-the-art performance on image classification tasks, but provide no (appropriate) measure of confidence in their predictions. We show how it is possible to use Gaussian Processes on image embeddings generated by a CNN to improve variance estimation in prediction.
