  Neural networks for image classification tasks assume that any given image
during inference belongs to one of the training classes. This closed-set
assumption is challenged in real-world applications where models may encounter
inputs of unknown classes. Open-set recognition aims to solve this problem by
rejecting unknown classes while classifying known classes correctly. In this
paper, we propose to utilize gradient-based representations obtained from a
known classifier to train an unknown detector with instances of known classes
only. Gradients correspond to the amount of model updates required to properly
represent a given sample, which we exploit to understand the model's capability
to characterize inputs with its learned features. Our approach can be utilized
with any classifier trained in a supervised manner on known classes without the
need to model the distribution of unknown samples explicitly. We show that our
gradient-based approach outperforms state-of-the-art methods by up to 11.6% in
open-set classification.
