原型邻近网络具有特定任务的增强型超级学习,用于短暂的分类
Zhen Jiang1, Zeyu Feng1, Bolin Niu1
1School of computer science and communication engineering, Jiangsu University, Zhenjiang, PR China.
概括
原型邻近网络 (PNN) 通过将原型网络与邻近网络相结合,增强了少数镜头分类 (FSC). 这种方法改善了度量学习,以更好地对有限的数据进行分类.
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:
- 少数镜头分类 (FSC) 依赖于原型网络 (PN),但它们的单模原型可能无法捕捉复杂的数据分布.
- 现有的方法在有限的标记数据中扎,影响了模型的代表性.
研究的目的:
- 引入原型-邻近网络 (PNN) 以改进少数镜头分类.
- 增强元学习机制,以更好地适应新课程.
- 为FSC开发一种新的数据增强方法.
主要方法:
- 建议邻居网络 (NN) 以邻居为基础对样本进行分类,并优化度量空间.
- 将PN和NN结合到PNN中,以获得具有有限数据的强大度量学习.
- 结合特定任务的微调和PN-NN数据增强技术来减少伪标签噪声.
主要成果:
- 在mini-imageNet和CUB数据集上,PNN的性能优于24个最先进的FSC算法.
- 在分层-imageNet.Net上取得了竞争性结果.
- 在四个跨领域医学图像数据集上证明了有效性.
结论:
- 使用有限的数据,PNN学习了用于Few-Shot分类的高级度量空间.
- 增强的元学习和数据增强可以提高模型的概括性,减少噪音.
- PNN在一般和跨领域的FSC任务方面都有很大的潜力.
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