垃圾变宝藏：利用开集半监督学习的跨模态匹配收获OOD数据
Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learning
摘要：
开集半监督学习（开集SSL）研究了一个具有挑战性但实际的场景，其中分布外（OOD）样本包含在未标记的数据中。虽然主流技术旨在完全过滤出OOD样本的半监督学习（SSL），我们提出了一种新的训练机制，可以有效地利用OOD数据的存在，增强功能学习，同时避免其对SSL的不利影响。我们通过首先引入一个热身训练来实现这个目标，该训练利用了所有未标记的数据，包括分发（ID）和OOD样本。具体来说，我们执行一个借口任务，强制我们的特征提取器获得对训练图像的高级语义理解，从而产生更多的区分特征，使下游任务受益。由于OOD样本不可避免地对SSL有害，我们提出了一种新的跨模态匹配策略来检测OOD样本。我们不是直接应用二元分类，而是训练网络来预测数据样本是否与指定的独热类别标签匹配。所提出的跨模态匹配在二进制分类上的吸引力在于能够生成与核心分类任务一致的兼容特征空间。大量的实验表明，我们的方法大大提高了性能的开集SSL和优于国家的最先进的大幅度。
英文摘要：
Open-set semi-supervised learning (open-set SSL) investigates a challenging but practical scenario where out-of-distribution (OOD) samples are contained in the unlabeled data. While the mainstream technique seeks to completely filter out the OOD samples for semi-supervised learning (SSL), we propose a novel training mechanism that could effectively exploit the presence of OOD data for enhanced feature learning while avoiding its adverse impact on the SSL. We achieve this goal by first introducing a warm-up training that leverages all the unlabeled data, including both the in-distribution (ID) and OOD samples. Specifically, we perform a pretext task that enforces our feature extractor to obtain a high-level semantic understanding of the training images, leading to more discriminative features that can benefit the downstream tasks. Since the OOD samples are inevitably detrimental to SSL, we propose a novel cross-modal matching strategy to detect OOD samples. Instead of directly applying binary classification, we train the network to predict whether the data sample is matched to an assigned one-hot class label. The appeal of the proposed cross-modal matching over binary classification is the ability to generate a compatible feature space that aligns with the core classification task. Extensive experiments show that our approach substantially lifts the performance on open-set SSL and outperforms the state-of-the-art by a large margin.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
