We conduct a comprehensive evaluation of state-of-the-art deep active
learning methods. Surprisingly, under general settings, no single-model method
decisively outperforms entropy-based active learning, and some even fall short
of random sampling. We delve into overlooked aspects like starting budget,
budget step, and pretraining's impact, revealing their significance in
achieving superior results. Additionally, we extend our evaluation to other
tasks, exploring the active learning effectiveness in combination with
semi-supervised learning, and object detection. Our experiments provide
valuable insights and concrete recommendations for future active learning
studies. By uncovering the limitations of current methods and understanding the
impact of different experimental settings, we aim to inspire more efficient
training of deep learning models in real-world scenarios with limited
annotation budgets. This work contributes to advancing active learning's
efficacy in deep learning and empowers researchers to make informed decisions
when applying active learning to their tasks.

通过全面评估当前最先进的深度主动学习方法，我们的研究发现在一般情况下没有单一模型的方法能显著超越基于熵的主动学习方法，甚至一些方法还不如随机抽样。我们深入研究了常常被忽视的方面，如初始预算、预算递增步长和预训练的影响，并揭示了它们在实现卓越结果上的重要性。此外，我们还将评估拓展到其他任务，探索了将主动学习与半监督学习和目标检测相结合的有效性。我们的实验提供了有价值的见解和具体建议，为未来的主动学习研究做出了贡献。通过揭示当前方法的局限性，并理解不同实验设置的影响，我们旨在启发在有限注释预算的现实场景下更高效地训练深度学习模型。这项工作有助于提高主动学习在深度学习中的效果，并使研究人员在应用主动学习时能做出明智的决策。