小样本学习下的特征中心对齐域适应算法

域适应是一种在训练集和测试集不满足独立同分布条件时使用的迁移学习算法.当两个领域间的分布差异较大时,会降低域内可迁移性,并且现有域适应算法需要获取大量的目标域数据,这在一些实际应用中无法实现.针对现有域适应方法的不足,基于卷积神经网络提出小样本学习下的基于特征中心对齐的域适应算法,寻找域不变特征的同时,提高目标域特征的可区分度,提高分类效果.面向小样本条件下的office-31公共数据集识别和雷达工作模式识别的仿真实验结果表明,所提方法对office-31数据集的平均识别精度比最大均值差异方法提升12.9%,而对雷达工作模式识别精度达到91%,比最大均值差异方法性能提升10%.

Domain Adaptation Algorithm with Feature Center Alignment for Few-shot Learning

Domain adaptation is a transfer learning algorithm used when the training and test sets do not satisfy the independent homogeneous distribution condition. When the distribution difference between two domains is large, the intra-domain transferability will be reduced, and the existing domain adaptation algorithms need to obtain a large amount of target domain data, which cannot be achieved in some practical applications. In view of the shortcomings of existing domain adaptation methods, the convolutional neural network model is used, and a domain adaptation algorithm based on feature center alignment for few-shot learning is proposed to find domain invariant features, improve the distinguishability of target domain features, and strengthen the classification accuracy. Simulation and experimental results for office-31 public dataset recognition and radar working pattern recognition under small sample conditions show that the proposed method improves the average recognition accuracy of the office-31 dataset by 12.9% compared with the maximum mean discrepancy method, and the radar working pattern recognition accuracy reaches 91%, which is 10% better than the maximum mean discrepancy method.