论文标题
做出首选：解决视力积极学习中的冷启动问题
Making Your First Choice: To Address Cold Start Problem in Vision Active Learning
论文作者
论文摘要
主动学习有望通过迭代选择要首先注释的最重要数据来提高注释效率。但是，我们发现了与这一诺言的惊人矛盾：主动学习无法像前几个选择一样有效地选择数据。我们将其确定为视觉主动学习中的冷启动问题，这是由有偏见且异常的初始查询引起的。本文试图通过利用对比度学习的三个优势来解决冷门问题：（1）不需要注释； （2）伪标记确保标签多样性来减轻偏见； （3）典型数据由对比特征确定以减少异常值。实验是在CIFAR-10-LT和三个医学成像数据集（即结肠病理学，腹部CT和血细胞显微镜）上进行的。我们的初始查询不仅显着胜过现有的主动查询策略，而且还超过了随机选择。我们预见到解决冷启动问题的解决方案是一个简单而强大的基线，可以选择视力主动学习的初始查询。可用代码：https：//github.com/c-liangyu/csval
Active learning promises to improve annotation efficiency by iteratively selecting the most important data to be annotated first. However, we uncover a striking contradiction to this promise: active learning fails to select data as efficiently as random selection at the first few choices. We identify this as the cold start problem in vision active learning, caused by a biased and outlier initial query. This paper seeks to address the cold start problem by exploiting the three advantages of contrastive learning: (1) no annotation is required; (2) label diversity is ensured by pseudo-labels to mitigate bias; (3) typical data is determined by contrastive features to reduce outliers. Experiments are conducted on CIFAR-10-LT and three medical imaging datasets (i.e. Colon Pathology, Abdominal CT, and Blood Cell Microscope). Our initial query not only significantly outperforms existing active querying strategies but also surpasses random selection by a large margin. We foresee our solution to the cold start problem as a simple yet strong baseline to choose the initial query for vision active learning. Code is available: https://github.com/c-liangyu/CSVAL
