Opening Deep Neural Networks with Generative Models

Image classification methods are usually trained to perform predictions taking into account a predefined group of known classes. Real-world problems, however, may not allow for a full knowledge of the input and label spaces, making failures in recognition a hazard to deep visual learning. Open set r...

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Bibliographic Details
Published inarXiv.org
Main Authors Vendramini, Marcos, Oliveira, Hugo, Machado, Alexei, dos Santos, Jefersson A
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 30.06.2021
Subjects
Algorithms
Artificial neural networks
Image classification
Machine learning
Neural networks
Object recognition
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Summary:Image classification methods are usually trained to perform predictions taking into account a predefined group of known classes. Real-world problems, however, may not allow for a full knowledge of the input and label spaces, making failures in recognition a hazard to deep visual learning. Open set recognition methods are characterized by the ability to correctly identify inputs of known and unknown classes. In this context, we propose GeMOS: simple and plug-and-play open set recognition modules that can be attached to pretrained Deep Neural Networks for visual recognition. The GeMOS framework pairs pre-trained Convolutional Neural Networks with generative models for open set recognition to extract open set scores for each sample, allowing for failure recognition in object recognition tasks. We conduct a thorough evaluation of the proposed method in comparison with state-of-the-art open set algorithms, finding that GeMOS either outperforms or is statistically indistinguishable from more complex and costly models.
ISSN:2331-8422