Open Category Classification by Adversarial Sample Generation

Yang Yu, Wei-Yang Qu, Nan Li, Zimin Guo
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
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
more » ... s of seen categories in the unsupervised manner via an adversarial learning strategy. With the generated samples, ASG then learns to tell seen from unseen in the supervised manner. Experiments performed on several datasets show the effectiveness of ASG.
doi:10.24963/ijcai.2017/469 dblp:conf/ijcai/YuQLG17 fatcat:gytbgngnxveo5k7u43hl6ctogm