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Generative OpenMax for Multi-Class Open Set Classification

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arxiv 1707.07418 v1 pith:JA2L56IP submitted 2017-07-24 cs.CV

ZongYuan Ge , Sergey Demyanov , Zetao Chen , Rahil Garnavi This is my paper · ORCID
classification cs.CV
keywords classesmethodopenopenmaxclassificationmulti-classunknownapproach
verification ladder T0 review T1 audit T2 compute T3 formal
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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 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Baseline Study and Benchmark for Few-Shot Open-Set Action Recognition with Feature Residual Discrimination

    cs.CV 2026-03 conditional novelty 5.0 of 10

    A feature-residual discriminator adapted from skeleton-based recognition improves unknown-action rejection over standard baselines on a new five-dataset benchmark for few-shot open-set action recognition.

  2. Open-Set LiDAR Panoptic Segmentation Guided by Uncertainty-Aware Learning

    cs.CV 2025-06 conditional novelty 5.0 of 10

    Uncertainty-guided LiDAR panoptic segmentation (ULOPS) uses evidential learning and three uncertainty losses to segment unknown objects, outperforming prior open-set baselines on KITTI-360 and nuScenes.

  3. Bridging Distribution Shift and AI Safety: Conceptual and Methodological Synergies

    cs.LG 2025-05 conditional novelty 5.0 of 10

    The paper proposes a one-to-one mapping between six causes of distribution shift and several AI safety issues, arguing for mutual method transfer through aligned definitions.

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