论文标题
迈向现实的半监督学习
Towards Realistic Semi-Supervised Learning
论文作者
论文摘要
深度学习正在推动许多计算机视觉应用中的最新技术。但是，它依赖于大量注释的数据存储库，并且捕获现实世界数据的不受约束性质尚未解决。半监督学习（SSL）用大量未标记的数据来补充带注释的培训数据，以降低注释成本。标准SSL方法假设未标记的数据来自与注释数据相同的分布。最近，引入了一个更现实的SSL问题，称为open-world ssl，未注释的数据可能包含来自未知类别的样本。在本文中，我们提出了一种基于伪标签的新方法，以在开放世界中处理SSL。在我们方法的核心上，我们利用样本不确定性并将有关类分布的先验知识纳入，以生成可靠的类别 - 分布感知的伪标签，用于属于已知和未知类别的未标记数据。我们的广泛实验在几个基准数据集上展示了我们的方法的有效性，在该数据集上，它在其中的七个不同数据集（包括CIFAR-100（〜17％），Imagenet-100（〜5％）（〜5％）和Tiny ImageNet（〜9％）上的七个不同数据集上的现有最新作品大大优于现有的最新技术。我们还强调了我们在解决新颖的类发现任务，展示其处理不平衡数据方面的稳定性方面的灵活性
Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data. Recently, a more realistic SSL problem, called open-world SSL, is introduced, where the unannotated data might contain samples from unknown classes. In this paper, we propose a novel pseudo-label based approach to tackle SSL in open-world setting. At the core of our method, we utilize sample uncertainty and incorporate prior knowledge about class distribution to generate reliable class-distribution-aware pseudo-labels for unlabeled data belonging to both known and unknown classes. Our extensive experimentation showcases the effectiveness of our approach on several benchmark datasets, where it substantially outperforms the existing state-of-the-art on seven diverse datasets including CIFAR-100 (~17%), ImageNet-100 (~5%), and Tiny ImageNet (~9%). We also highlight the flexibility of our approach in solving novel class discovery task, demonstrate its stability in dealing with imbalanced data, and complement our approach with a technique to estimate the number of novel classes
