Unseen Class Discovery in Open-world Classification

Abstract

This paper concerns open-world classification, where the classifier not only needs to classify test examples into seen classes that have appeared in training but also reject examples from unseen or novel classes that have not appeared in training. Specifically, this paper focuses on discovering the hidden unseen classes of the rejected examples. Clearly, without prior knowledge this is difficult. However, we do have the data from the seen training classes, which can tell us what kind of similarity/difference is expected for examples from the same class or from different classes. It is reasonable to assume that this knowledge can be transferred to the rejected examples and used to discover the hidden unseen classes in them. This paper aims to solve this problem. It first proposes a joint open classification model with a sub-model for classifying whether a pair of examples belongs to the same or different classes. This sub-model can serve as a distance function for clustering to discover the hidden classes of the rejected examples. Experimental results show that the proposed model is highly promising.

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Cited by in corpus (13)

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• One-vs-Rest Network-based Deep Probability Model for Open Set Recognition
• Classification-Reconstruction Learning for Open-Set Recognition
• OneFlow: One-class flow for anomaly detection based on a minimal volume region
• Making Good on LSTMs' Unfulfilled Promise
• Semi-Supervised Class Discovery
• Self-Initiated Open World Learning for Autonomous AI Agents
• MMF: A loss extension for feature learning in open set recognition
• Open-World Class Discovery with Kernel Networks
• Deep Learning and Open Set Malware Classification: A Survey