融合零样本学习和小样本学习的弱监督学习方法综述

doi:10.3969/j.issn.1001-506X.2020.10.13

Abstract

Abstract:

The deep learning model relies heavily on a large amount of human-annotated data, which seriously restricts its application in special fields where data is scarce. Facing practical challenges such as lack of data, many researchers have conducted research on the weakly supervised learning method which is weakly data-dependent, and some typical research directions such as few-shot learning and zero-shot learning have emerged. In this regard, this paper mainly introduces the few-shot learning and zero-shot learning under the condition of the weakly supervised learning method, including the problem definition, the current mainstream methods and the experimental design scheme, and the classification performances of typical models are compared. Then, the problem description of zero-to-few-shot learning is given, the current research status and experimental design are summarized, and the performances of typical methods are compared. Finally, based on the problems in the current research, the future research direction is prospected, including the fusion of multiple weakly supervised learning methods and the exploration of theoretical basis, as well as the application in other fields.

Key words: weakly supervised learning, few-shot learning, zero-shot learning, zero-to-few-shot learning

CLC Number:

TP 391.41

Chongyu PAN, Jian HUANG, Jianguo HAO, Jianxing GONG, Zhongjie ZHANG. Survey of weakly supervised learning integrating zero-shot and few-shot learning[J]. Systems Engineering and Electronics, 2020, 42(10): 2246-2256.

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方法	机制	优点	缺点	适用范围
基于度量的方法	在某一度量空间中直接进行对比匹配	模型直观,易于理解	训练集样本数量要求相对较高, 性能对模型结构敏感度高	支持样本数据量较大的情况
基于优化的方法	通过模型自适应优化使之适应新任务	模型具备快速适应新任务的能力	测试任务需和训练任务实验设置相同,灵活性较差	测试环境和训练环境具有相同设置的情况
基于生成式模型的方法	通过生成新类别样本, 转化为监督学习问题	分步执行,易于追溯	生成样本代表性问题, 模型训练困难	类别数及支持样本数量较小的情况
基于图神经网络的方法	通过边和节点迭代更新各样本及其之间关系	模型结构简单	计算量随支持样本数增加激增	类别数及支持样本数量较小的情况
基于记忆单元的方法	通过动态更新内存状态实现新类别学习	模型可动态更新	额外增加外置记忆单元,增大内存需求	类别数及支持样本数量较小的情况

模型	特征提取器结构	5-way 1-shot/%	5-way 5-shot/%
Prototypical Network^[6]	64(3)-64(3)-64(3)-64(3)	49.42	68.20
Matching Network^[5]	Inception Network	46.6	60.0
Relation Network^[7]	64(3)-64(3)-64(3)-64(3)	57.02	71.07
MAML^[9]	32(3)-32(3)-32(3)-32(3)	48.70	63.11
MTL^[25]	ResNet-12	61.2	75.5
Δ-encoder^[26]	VGG16	59.9	69.7
MM-Net^[23]	64(3)-64(3)-64(3)-64(3)	53.35	66.97
GNN^[19]	64(3)-96(3)-128(3)-256(3)	50.33	66.41
EGNN^[27]	64(3)-96(3)-128(3)-256(3)	-	66.85
IDeMe-Net^[28]	ResNet-18	59.14	74.63

方法	机制	优点	缺点	适用范围
度量学习方法	在某一特征空间中进行最近邻匹配	模型直观,易于理解	模型性能受度量空间影响较大	训练集数量较大
兼容性学习方法	直接计算两个特征空间向量的相似度	模型简单, 计算量较小	训练集数据量要求较高	训练集数量较大
基于流形结构的方法	在特征空间中进行流形结构迁移	模型能够整体考虑类别间关联关系	不同特征空间的流形结构存在异构性,较难迁移	训练数据和测试数据类别结构相似度较高
基于生成式模型的方法	通过生成新类别样本, 转化为监督学习问题	分步执行,易于追溯	生成样本代表性问题, 模型训练困难	类别数及支持样本数量较小的情况

数据集	类别总数	样本总数	训练集/测试集类别数	类别描述特征向量
AWA	50	304 75	40/10	85-D Attributes
CUB	200	117 88	150/50	312-D Attributes
ImageNet 2010	1 000	大于 1.2×10⁶	800/200	500-D Word Embedding

模型	特征提取器结构	AWA/%	CUB/%	ImageNet 2010(hit@5)/%
ALE^[47]	SIFT	48.5	26.9	-
SJE^[46]	GoogLeNet	60.1	29.9	-
LatEm^[48]	GoogLeNet	72.5	45.6	-
SAE^[67]	GoogLeNet	57.9	44.8	-
RKT^[54]	GoogLeNet	82.43	46.24	-
CDL^[39]	ResNet-101	69.9	54.5	-
DeViSE^[45]	Alexnet	-	-	31.8
AMP^[68]	AlexNet	66.0	-	41.0
EXEM^[38]	GoogLeNet	76.5	58.5	-
LAD^[38]	VGGNet-19	82.48	56.63	-
SE-ZSL^[59]	VGGNet-19	83.8	60.3	-
DEM^[35]	Inception-V2	88.1	59.0	60.7
SYNC^[49]	GoogLeNet	72.9	54.7	-
MFMR^[69]	GoogLeNet	79.8	47.7	-
UVDS^[56]	VGGNet-19	82.12	45.72	-

Survey of weakly supervised learning integrating zero-shot and few-shot learning

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模型	特征提取器结构	miniImageNet		tieredImageNet
模型	特征提取器结构	5-way 1-shot	5-way 5-shot	5-way 1-shot	5-way 5-shot
DeViSE^[45]	Alexnet	56.99	72.63	61.78	77.17
ReViSE^[73]	GoogLeNet	57.23	73.85	62.77	77.27
CADA-VAE^[70]	ResNet-101	61.59	75.63	63.16	78.86
AM3- ProtoNets++^[71]	ResNet-12	65.21	75.20	67.23	78.95
AM3-TADAM^[71]	ResNet-12	65.30	78.10	69.08	82.58

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算法 1 Episode-based meta training
步骤 1 在训练集中构建M-way N-shot Q-query子任务数据集D^tr;
步骤 2 基于设计好的模型及D^tr,前向计算模型损失函数Loss;
步骤 3 基于子任务损失函数Loss更新模型参数;
步骤 4 返回步骤1,直至达到迭代次数。