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An Autuencoder-based Data Augmentation Strategy for Generalization Improvement of DCNNs
Neurocomputing ( IF 6 ) Pub Date : 2020-08-01 , DOI: 10.1016/j.neucom.2020.03.062
Xiexing Feng , Q.M. Jonathan Wu , Yimin Yang , Libo Cao

Abstract Inspired by the phenomenon that the decoding weights of a well-trained autoencoder contain the information of the training samples, we proposed a data augmentation method by utilizing the decoding weights. Given a batch of training data, the autoencoder is trained and the decoding weights are activated; the decoding weights are then combined with the raw samples to generate augmented samples. Furthermore, we probe its working mechanism in three ways: (i) we prove that the decoding weights and the raw samples are of linear relationship under the transformation of a certain invertible function; (ii) the proposed method can sample from a larger range in both feature dimensions and label dimension, which can be interpreted as a broader distribution vicinity compared with those by other approaches; (iii) the model trained with our data augmentation approach has better representation capability, which is reflected by the higher Fisher’s criteria value in deep feature space. We conduct extensive experiments on image and tabular dataset with multiple network architectures. The proposed method provides significant generalization performance improvement compared with the baseline and better or comparable performance compared with the other state-of-the-art data augmentation approaches.

中文翻译:

用于 DCNN 泛化改进的基于自动编码器的数据增强策略

摘要 受训练好的自编码器的解码权重包含训练样本信息这一现象的启发,我们提出了一种利用解码权重的数据增强方法。给定一批训练数据,训练自动编码器并激活解码权重;然后将解码权重与原始样本组合以生成增强样本。此外,我们从三个方面探讨了它的工作机制:(i)我们证明了解码权重与原始样本在某个可逆函数的变换下呈线性关系;(ii) 所提出的方法可以从特征维度和标签维度的更大范围进行采样,与其他方法相比,可以将其解释为更广泛的分布附近;(iii) 用我们的数据增强方法训练的模型具有更好的表示能力,这体现在深层特征空间中较高的 Fisher 标准值。我们对具有多种网络架构的图像和表格数据集进行了大量实验。与基线相比,所提出的方法提供了显着的泛化性能改进,与其他最先进的数据增强方法相比,具有更好或可比的性能。
更新日期:2020-08-01
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