Open-Set Recognition with Gradient-Based Representations

• URL: http://arxiv.org/abs/2206.08229v1
• Date: Thu, 16 Jun 2022 14:54:12 GMT
• Title: Open-Set Recognition with Gradient-Based Representations
• Authors: Jinsol Lee, Ghassan AlRegib
• Abstract summary: We propose to utilize gradient-based representations to train an unknown detector with instances of known classes only. We show that our gradient-based approach outperforms state-of-the-art methods by up to 11.6% in open-set classification.
• Score: 16.80077149399317
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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