Open-Set Recognition with Gradient-Based Representations
This addresses the challenge of real-world image classification where models encounter unknown classes, offering a practical solution without needing explicit unknown sample modeling.
The paper tackles the problem of open-set recognition, where models must reject unknown classes while correctly classifying known ones, by proposing a gradient-based representation method that outperforms state-of-the-art approaches by up to 11.6% in classification accuracy.
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.