2022/09/28 by Haoran Li, Chun-Mei Feng, Li, Haoran +7
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Infrastructure Maintenance and Monitoring
paper · pdf · doi:10.48550/arxiv.2209.14205
openalex publication_date 2022/09/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Open-set semi-supervised learning (OSSL) has attracted growing interest, which investigates a more practical scenario where out-of-distribution (OOD) samples are only contained in unlabeled data. Existing OSSL methods like OpenMatch learn an OOD detector to identify outliers, which often update all modal parameters (i.e., full fine-tuning) to propagate class information from labeled data to unlabeled ones. Currently, prompt learning has been developed to bridge gaps between pre-training and fine-tuning, which shows higher computational efficiency in several downstream tasks. In this paper, we propose a prompt-driven efficient OSSL framework, called OpenPrompt, which can propagate class information from labeled to unlabeled data with only a small number of trainable parameters. We propose a prompt-driven joint space learning mechanism to detect OOD data by maximizing the distribution gap between ID and OOD samples in unlabeled data, thereby our method enables the outliers to be detected in a new way. The experimental results on three public datasets show that OpenPrompt outperforms state-of-the-art methods with less than 1% of trainable parameters. More importantly, OpenPrompt achieves a 4% improvement in terms of AUROC on outlier detection over a fully supervised model on CIFAR10.