Generative OpenMax for Multi-Class Open Set Classification

Abstract

We present a conceptually new and flexible method for multi-class open set classification. Unlike previous methods where unknown classes are inferred with respect to the feature or decision distance to the known classes, our approach is able to provide explicit modelling and decision score for unknown classes. The proposed method, called Gener- ative OpenMax (G-OpenMax), extends OpenMax by employing generative adversarial networks (GANs) for novel category image synthesis. We validate the proposed method on two datasets of handwritten digits and characters, resulting in superior results over previous deep learning based method OpenMax Moreover, G-OpenMax provides a way to visualize samples representing the unknown classes from open space. Our simple and effective approach could serve as a new direction to tackle the challenging multi-class open set classification problem.

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Cited by in corpus (9)

• Recent Advances in Open Set Recognition: A Survey
• Open Set Recognition with Conditional Probabilistic Generative Models
• Hybrid Models for Open Set Recognition
• Few-Shot Open-Set Recognition using Meta-Learning
• GB-CosFace: Rethinking Softmax-based Face Recognition from the Perspective of Open Set Classification
• P-ODN: Prototype based Open Deep Network for Open Set Recognition
• Learning without Seeing nor Knowing: Towards Open Zero-Shot Learning
• Towards Robust Pattern Recognition: A Review
• Non-Exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks