为持续的无监督域名适应进行生成外观重播
Boqi Chen1, Kevin Thandiackal2, Pushpak Pati3
1ETH AI Center, Zurich, Switzerland; Department of Computer Science, ETH Zurich, Switzerland.
Medical image analysis
|August 19, 2023
概括
这项研究介绍了持续域调整 (GarDA) 的生成外观重复,这是一种用于无监督细分的新方法. GarDA将模型顺序调整到新的数据域,而不需要过去的数据,克服隐私和数据限制.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 深度学习在大型标记数据集中表现出色,但现实世界的数据带来了诸如增量可用性,域名转移和缺乏标签等挑战.
- 医疗应用程序面临严格的隐私法规,禁止保留以前看到的数据.
- 无监督域调整 (UDA) 对于在不同域中利用无标签数据至关重要.
研究的目的:
- 在涉及领域转移的持续学习场景中,解决无监督细分的挑战.
- 开发一种方法,通过使用未标记的数据,将细分模型连续适应到新领域.
- 为了使适应不需要访问先前看到的数据,增强实际适用性.
主要方法:
- 介绍了GarDA (Generative Appearance Replay for Continual Domain Adaptation),一种基于生成重复的方法. 这是一个基于生成重复的方法.
- 对细分模型的顺序适应新领域的未标记数据.
- 通过不断的调整,利用和整合来自多个领域的信息.
主要成果:
- 在多个领域的无监督细分方面,GarDA的性能远远超过现有的技术.
- 该方法在持续学习场景中表现出有效性,具有域名转移.
- 在涉及不同机关和方式的三个数据集上成功进行了评估.
结论:
- GarDA为隐私受限,持续学习设置中的无监督细分提供了一个实用的解决方案.
- 生成式重播方法有效地处理域名转移而无需数据保留.
- 这项工作通过消除对事先数据访问的需求,推动了增量UDA领域的发展.
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