双蒸区分器网络用于域自适应的几击学习
Xiyao Liu1, Zhong Ji2, Yanwei Pang2
1State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China.
概括
域自适应性短暂学习 (DA-FSL) 使用双蒸区分器网络 (D3Net) 来改进新域的分类. 通过生成目标样本和调整域分布,D3Net克服了数据不平衡.
科学领域:
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 域自适应性短暂学习 (DA-FSL) 解决了使用有限的目标数据在新领域的分类挑战.
- 关键的挑战包括将知识从源域转移到目标域,以及处理数据不对称.
研究的目的:
- 为DA-FSL提出双蒸区分器网络 (D3Net),特别解决标记目标域样本的稀缺问题.
- 通过生成合成目标样本和对准域分布来提高目标域的性能.
主要方法:
- D3Net采用蒸歧视,以防止来自不平等的来源和目标域样本大小的过.
- 任务传播阶段和混合域阶段旨在生成更多的目标样本,利用源域分布和多样性.
- 在源域和目标域之间实现了分布对齐,通过原型分布限制了少数射击学习 (FSL) 任务分布.
主要成果:
- 在 DA-FSL 基准数据集上,D3Net 展示了竞争性表现:迷你图像网,分层图像网和DomainNet.
- 提出的方法有效地解决了DA-FSL中的数据不对称问题.
结论:
- 通过有效生成目标样本和对准域分布,D3Net为域自适应性短暂学习提供了强大的解决方案.
- 这种方法显着有望改善在具有有限标记数据的新领域的少数镜头分类.
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