论文标题
为数字病理和自然图像提供有效的注释有效学习（AEL）
Embracing Annotation Efficient Learning (AEL) for Digital Pathology and Natural Images
论文作者
论文摘要
Jitendra Malik曾经说过：“监督是AI研究人员的鸦片”。大多数深度学习技术都在很大程度上依赖大量的人类标签来有效工作。在当今世界，数据创建速率大大超过了数据注释的速度。完全依赖人类注释只是解决当前AI中当前封闭问题的临时手段。实际上，只有一小部分数据被注释。注释有效学习（AEL）是对有效训练模型的算法的研究，并减少了注释。为了在AEL环境中蓬勃发展，我们需要较少依赖手动注释（例如，图像，边界框和每个像素标签）的深度学习技术，但要从未标记的数据中学习有用的信息。在本文中，我们探索了处理AEL的五种不同技术。
Jitendra Malik once said, "Supervision is the opium of the AI researcher". Most deep learning techniques heavily rely on extreme amounts of human labels to work effectively. In today's world, the rate of data creation greatly surpasses the rate of data annotation. Full reliance on human annotations is just a temporary means to solve current closed problems in AI. In reality, only a tiny fraction of data is annotated. Annotation Efficient Learning (AEL) is a study of algorithms to train models effectively with fewer annotations. To thrive in AEL environments, we need deep learning techniques that rely less on manual annotations (e.g., image, bounding-box, and per-pixel labels), but learn useful information from unlabeled data. In this thesis, we explore five different techniques for handling AEL.
