Open-Category Classification by Adversarial Sample Generation

Understand

In real-world classification tasks, it is difficult to collect training samples from all possible categories of the environment.

• Therefore, when an instance of an unseen class appears in the prediction stage, a robust classifier should be able to tell that it is from an unseen class, instead of classifying it to be any known category.
• In this paper, adopting the idea of adversarial learning, we propose the ASG framework for open-category classification.
• ASG generates positive and negative samples of seen categories in the unsupervised manner via an adversarial learning strategy.