基于梯形网络和改进三训练法的半监督分类

莫建文; 贾鹏

doi:10.16383/j.aas.c190869

基于梯形网络和改进三训练法的半监督分类

doi: 10.16383/j.aas.c190869

莫建文^1,,
贾鹏^1,

1.
桂林电子科技大学信息与通信学院桂林 541000

基金项目: 国家自然科学基金(61661017, 61967005, U1501252), 广西自然科学基金(2017GXNSFBA198212), 桂林电子科技大学研究生创新项目基金(2019YCXS020)资助

详细信息

作者简介:
莫建文：桂林电子科技大学信息与通信学院副教授. 主要研究方向为机器视觉, 智能图像处理. 本文通信作者. E-mail: mo_jianwen@126.com

贾鹏：桂林电子科技大学信息与通信学院硕士研究生. 主要研究方向为智能图像处理. E-mail: jiapeng_jay@163.com

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出版历程
- 收稿日期: 2019-12-20
- 修回日期: 2020-05-22
- 网络出版日期: 2022-07-12
- 刊出日期: 2022-06-01

Semi-supervised Classification Model Based on Ladder Network and Improved Tri-training

MO Jian-Wen^1
,,
JIA Peng^1
,

1.
School of Information and Communications, Guilin University of Electronic Science and Technology, Guilin 541000

Funds: Supported by National Natural Science Foundation of China (61661017, 61967005, U1501252), Natural Science Foundation of Guangxi (2017GXNSFBA198212), and Postgraduate Innovation Project of Guilin University of Electronic Science and Technology (2019YCXS020)

More Information

Author Bio:
MO Jian-Wen 　Associate professor at the School of Information and Communication, Guilin University of Electronic Science and Technology. His research interest covers machine vision and intelligent image processing. Corresponding author of this paper

JIA Peng　 Master student at the School of Information and Communication, Guilin University of Electronic Science and Technology. His main research interest is intelligent image processing

摘要

摘要: 为了提高半监督深层生成模型的分类性能, 提出一种基于梯形网络和改进三训练法的半监督分类模型. 该模型在梯形网络框架有噪编码器的最高层添加3个分类器, 结合改进的三训练法提高图像分类性能. 首先, 用基于类别抽样的方法将有标记数据分为3份, 模型以有标记数据的标签误差和未标记数据的重构误差相结合的方式调整参数, 训练得到3个Large-margin Softmax分类器; 接着, 用改进的三训练法对未标记数据添加伪标签, 并对新的标记数据分配不同权重, 扩充训练集; 最后, 利用扩充的训练集更新模型. 训练完成后, 对分类器进行加权投票, 得到分类结果. 模型得到的梯形网络的特征有更好的低维流形表示, 可以有效地避免因为样本数据分布不均而导致的分类误差, 增强泛化能力. 模型分别在MNIST数据库, SVHN数据库和CIFAR10数据库上进行实验, 并且与其他半监督深层生成模型进行了比较, 结果表明本文所提出的模型得到了更高的分类精度.
- 梯形网络 /
- 改进的三训练法 /
- 半监督学习 /
- Large-margin Softmax分类器
Abstract: In order to improve the classification performance of semi-supervised deep generation models, a new model based on ladder network and improved tri-training algorithm is proposed. This model adds three classifiers to the highest layer of the noisy coding layer of the ladder network, and improves the image classification performance by combining the improved tri-training. First, the labeled data is divided into three parts by using a class-based sampling method. The model adjusts the parameters by combining the labeled error of labeled data and the reconstruction error of unlabeled data, and is trained to get three Large-margin Softmax classifiers. Next, the improved tri-training algorithm is developed to add pseudo-labels to the unlabeled data, and different weights are assigned to the new labeled data to expand the training set. Finally, the expanded training set is applied to update the model. After the above practice, a weighted voting is performed on the classifier to obtain the classification result. The characteristics of the ladder network obtained by this model have better low-dimensional manifold representations, which can effectively avoid classification error caused by uneven distribution of sample data and enhance generalization ability. The model is tested on the MNIST, SVHN and CIFAR10 respectively. In comparison with other semi-supervised deep generation models, test results show that the model proposed in this paper has obtained state-of-the-art classification accuracy over existing semi-supervised learning methods.
- Ladder network /
- improved tri-training /
- semi-supervised learning /
- Large-margin Softmax classifier

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图 1 LN-TT SSC模型

Fig. 1 LN-TT SSC model

下载: 全尺寸图片幻灯片

图 2 MNIST数据的流行表示图

Fig. 2 A popular representation of MNIST

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图 3 LN-TT SSC模型趋于收敛时的图片对比

Fig. 3 Comparison of image when the LN-TT SSC model tends to converge

下载: 全尺寸图片幻灯片

表 1 LN-TT SSC模型及混合方案在SVHN和CIFAR10数据集上的分类精度(%)

Table 1 Classification accuracy of LN-TT SSC model and hybrid scheme on SVHN and CIFAR10 (%)

模型 SVHN CIFAR10

基准实验 $(\Pi$+ softmax) 88.75 83.56
方案 A $(\Pi$+ LM-Softmax) 91.35 86.11
方案 B (VGG19 + softmax) 89.72 84.73
LN-TT SSC 93.05 88.64

下载: 导出CSV

表 2 MNIST数据集上的分类精度(%)

Table 2 Classification accuracy on MNIST dataset (%)

模型 ${N_1}$= 100 ${N_2}$= 1000

Ladder network^[11] 98.88 99.06
CAT-GAN^[8] 99.09 99.11
Improved-GAN^[9] 98.58 99.15
GAR^[19] 98.92 99.21
SSE-GAN^[10] 99.10 92.23
LN-TT SSC 99.14 99.30

下载: 导出CSV

表 3 SVHN数据集上的分类精度(%)

Table 3 Classification accuracy on SVHN dataset (%)

模型 ${N_1}$= 100 ${N_2}$= 1000

Ladder network^[11] 75.50 87.06
CAT-GAN^[8] 77.68 88.90
Improved-GAN^[9] 81.57 90.78
GAR^[19] 80.87 92.08
SSE-GAN^[10] 81.08 92.92
LN-TT SSC 82.13 93.05

下载: 导出CSV

表 4 CIFAR10数据集上的分类精度(%)

Table 4 Classification accuracy on CIFAR10 dataset (%)

模型 ${N_1}$= 2000 ${N_2}$= 4000

Ladder network^[11] 76.56 79.68
CAT-GAN^[8] 78.83 80.42
Temporal-Ensembling^[17] — 87.84
GAR^[19] 82.10 83.35
SSE-GAN^[10] 83.66 85.14
VAT^[21] 82.88 85.61
LN-TT SSC 84.14 88.64

下载: 导出CSV

参考文献(21)

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基于梯形网络和改进三训练法的半监督分类

doi: 10.16383/j.aas.c190869

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Semi-supervised Classification Model Based on Ladder Network and Improved Tri-training

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目录

模型	SVHN	CIFAR10
基准实验 $(\Pi$+ softmax)	88.75	83.56
方案 A $(\Pi$+ LM-Softmax)	91.35	86.11
方案 B (VGG19 + softmax)	89.72	84.73
LN-TT SSC	93.05	88.64

模型	${N_1}$= 100	${N_2}$= 1000
Ladder network^[11]	98.88	99.06
CAT-GAN^[8]	99.09	99.11
Improved-GAN^[9]	98.58	99.15
GAR^[19]	98.92	99.21
SSE-GAN^[10]	99.10	92.23
LN-TT SSC	99.14	99.30

模型	${N_1}$= 2000	${N_2}$= 4000
Ladder network^[11]	76.56	79.68
CAT-GAN^[8]	78.83	80.42
Temporal-Ensembling^[17]	—	87.84
GAR^[19]	82.10	83.35
SSE-GAN^[10]	83.66	85.14
VAT^[21]	82.88	85.61
LN-TT SSC	84.14	88.64

留言板

基于梯形网络和改进三训练法的半监督分类

doi: 10.16383/j.aas.c190869

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出版历程

Semi-supervised Classification Model Based on Ladder Network and Improved Tri-training

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