open set recognition

Open set recognition is a type of machine learning where a model learns to identify known categories and also recognizes when it encounters unfamiliar or unknown data. Unlike traditional systems that only classify predefined classes, open set recognition acknowledges that new, unseen data may appear during operation. It aims to correctly assign inputs to known categories when possible, but also to signal when something doesn’t fit any known category, reducing misclassification. This approach is especially useful in real-world applications where the environment is unpredictable and new kinds of data may emerge.