Learning Placeholders for Open-Set Recognition (2103.15086v1)

Abstract: Traditional classifiers are deployed under closed-set setting, with both training and test classes belong to the same set. However, real-world applications probably face the input of unknown categories, and the model will recognize them as known ones. Under such circumstances, open-set recognition is proposed to maintain classification performance on known classes and reject unknowns. The closed-set models make overconfident predictions over familiar known class instances, so that calibration and thresholding across categories become essential issues when extending to an open-set environment. To this end, we proposed to learn PlaceholdeRs for Open-SEt Recognition (Proser), which prepares for the unknown classes by allocating placeholders for both data and classifier. In detail, learning data placeholders tries to anticipate open-set class data, thus transforms closed-set training into open-set training. Besides, to learn the invariant information between target and non-target classes, we reserve classifier placeholders as the class-specific boundary between known and unknown. The proposed Proser efficiently generates novel class by manifold mixup, and adaptively sets the value of reserved open-set classifier during training. Experiments on various datasets validate the effectiveness of our proposed method.

• The paper’s main contribution is introducing data and classifier placeholders to transform closed-set training into an effective open-set recognition framework.
• It employs manifold mixup to simulate unknown class distributions, enabling adaptive calibration of classifiers without extra complexity.
• Experimental results demonstrate that OSER outperforms existing methods on benchmarks like CIFAR-10, SVHN, and Tiny-ImageNet in detecting unknown classes.

Learning Placeholders for Open-Set Recognition

The paper "Learning Placeholders for Open-Set Recognition" introduces a novel approach to the open-set recognition problem, which addresses the inherent limitations faced by traditional closed-set classifiers when encountering unknown instances in real-world applications. This research is centered around the development of a method termed "PlaceholdeRs for Open-SEt Recognition" (OSER), which significantly contributes to the domain of open-set recognition by emphasizing the calibration and adaptation of closed-set classifiers.

Core Contributions

The paper meticulously outlines two primary innovations:

1. Data Placeholders: To transform closed-set training into open-set training, the authors propose generating placeholders that anticipate the distribution of unknown classes. This is accomplished using manifold mixup, which simulates potential novel class instances by interpolating between hidden representations of different known instance classes. This technique allows the classifier to better anticipate novel patterns without incurring significant complexity costs, as it is merged with the regular training process.
2. Classifier Placeholders: The research introduces the concept of dummy classifiers that act as placeholders within the classifier itself, placing these between known and unknown classes to adaptively determine the threshold of recognition. By fine-tuning these classifiers to output the second-highest probabilities for known instances, the method efficiently calibrates the model to better separate known from unknown classes.

Technical Details and Results

The research tackles the challenge posed by overconfident predictions of traditional classifiers in an open-set environment, leveraging dummy classifiers to dynamically select thresholds based on instance-specific computations rather than a static threshold. This is substantiated through various experiments on benchmark datasets like CIFAR-10, SVHN, and Tiny-ImageNet.

The performance of the OSER method significantly outpaces existing methods such as G-OpenMax, OSRCI, and GFROSR. Particularly noteworthy is the improvement in mean AUC for unknown detection, surpassing previous state-of-the-art results by a substantial margin across datasets with differing degrees of openness. This validates the efficacy of the approach in different open-set recognition scenarios.

Implications and Future Directions

The practical implications of this research are considerable, particularly for real-world applications where adaptability to new classes is paramount. The OSER method poses potential advancements in various sectors, including automated surveillance, medical diagnostics, and anomaly detection systems where unknown instances must be accurately identified and handled.

On a theoretical level, this research paves the way for further exploration into dynamic threshold adjustment and the generation of novel instances in complex data environments. Future developments might explore how these placeholders can be extended to stream data contexts or their application in multi-task learning environments where new tasks or classes frequently arise.

In conclusion, the paper provides significant insights into the open-set recognition problem by innovatively addressing the calibration of closed-set classifiers. By adopting placeholders within both data and classifiers, the proposed OSER method represents a promising advance in AI's ability to effectively manage and adapt to unknown inputs.