Open Set

Open set refers to a situation in machine learning where a model encounters data that it has not seen before and may not recognize. Unlike closed set scenarios, where the model only needs to classify known categories, open set models are designed to acknowledge when a new, unfamiliar input does not belong to any existing class. This approach helps prevent misclassification by allowing the system to reject or flag unknown data, leading to more robust and realistic performance in real-world applications where new or unexpected inputs are common.