Adversarial Reciprocal Points Learning for Open Set Recognition (2103.00953v3)

Abstract: Open set recognition (OSR), aiming to simultaneously classify the seen classes and identify the unseen classes as 'unknown', is essential for reliable machine learning.The key challenge of OSR is how to reduce the empirical classification risk on the labeled known data and the open space risk on the potential unknown data simultaneously. To handle the challenge, we formulate the open space risk problem from the perspective of multi-class integration, and model the unexploited extra-class space with a novel concept Reciprocal Point. Follow this, a novel learning framework, termed Adversarial Reciprocal Point Learning (ARPL), is proposed to minimize the overlap of known distribution and unknown distributions without loss of known classification accuracy. Specifically, each reciprocal point is learned by the extra-class space with the corresponding known category, and the confrontation among multiple known categories are employed to reduce the empirical classification risk. Then, an adversarial margin constraint is proposed to reduce the open space risk by limiting the latent open space constructed by reciprocal points. To further estimate the unknown distribution from open space, an instantiated adversarial enhancement method is designed to generate diverse and confusing training samples, based on the adversarial mechanism between the reciprocal points and known classes. This can effectively enhance the model distinguishability to the unknown classes. Extensive experimental results on various benchmark datasets indicate that the proposed method is significantly superior to other existing approaches and achieves state-of-the-art performance.

• The paper introduces a novel Reciprocal Points concept that effectively delineates known from unknown classes to reduce open space risk.
• It employs adversarial margin constraints to tightly bound the embedding space and prevent overlap between known and unknown distributions.
• Experimental results on benchmarks such as MNIST, SVHN, and CIFAR show that ARPL significantly outperforms existing methods in open set recognition.

Adversarial Reciprocal Points Learning for Open Set Recognition

The paper "Adversarial Reciprocal Points Learning for Open Set Recognition" presents a novel learning framework aimed at improving the performance of open set recognition (OSR) by effectively managing the risks associated with known and unknown data classification. Open set recognition tasks necessitate a model's ability to correctly classify known classes while also identifying unseen classes as 'unknown'. This challenge involves reducing both the empirical classification risk and the open space risk, where the latter pertains to the incorrect classification of unknown data within the known classes' decision space.

The authors address this by proposing a concept known as the Reciprocal Point, which represents an unexploited area of the feature space that corresponds to the 'otherness' of each class. These points are pivotal in identifying classes within a multi-class setting by determining regions of the embedding space that should not be associated with known classes. The framework, termed Adversarial Reciprocal Point Learning (ARPL), employs adversarial techniques to refine the decision boundaries more effectively by pushing the known classes away from potential unknown regions and ensuring minimal overlap between known and unknown class distributions.

A significant aspect of this approach is the adversarial margin constraint, which limits the potential open space by adjusting a bounded margin, ensuring the latent embedding for each class remains within defined limits. By constraining the embedding space, ARPL effectively reduces open space risk through an interaction that not only pushes known classes away from their reciprocal points but also bounds them with respect to other classes, thus minimizing classification overlap with unknown categories.

The authors also propose an instantiated adversarial enhancement method to estimate and better characterize unknown distributions. This method generates trainable adversarial samples that closely mimic the latent characteristics of unknown data, further enhancing the model's ability to discern between known and unknown samples effectively by expanding the model's decision boundaries during training.

Experimental results show that the ARPL framework significantly outperformed existing methods on various benchmark datasets, achieving state-of-the-art results. For instance, it demonstrated strong performance on datasets such as MNIST, SVHN, and CIFAR variants, showing its robustness in detecting and managing unseen classes in practice. The inclusion of the adversarial generation of unknown samples served to bolster the model’s discriminability, particularly in complex open-world recognition scenarios.

The implications of this research are considerable for the advancement of AI, particularly in creating models that function reliably in real-world settings where unknown variables are a given constant. By effectively reducing the risk associated with open spaces and enhancing the model's generalization to new, unseen data, ARPL sets a new standard for OSR methodologies. The framework has applications in areas that require robust classification and anomaly detection capabilities, such as autonomous systems and security applications.

Future research may pivot on refining the ARPL methodology, perhaps by integrating more sophisticated adversarial techniques or hybrid modeling approaches to further tighten the bounds of the open space, thus enhancing generalization under increasingly diverse scenarios. Additionally, exploring dynamic reciprocal points for evolving datasets could provide further improvements to OSR tasks.