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In this paper, we proposed an efficient transferred Max-Slice CNN (MS-CNN) with L<jats:sup>2<\/jats:sup>-Regularization for SAR ATR, which could enrich the features and recognize the targets with superior performance. Firstly, the data amplification method is presented to reduce the computational time and enrich the raw features of SAR targets. Secondly, the proposed MS-CNN framework with L<jats:sup>2<\/jats:sup>-Regularization is trained to extract robust features, in which the L<jats:sup>2<\/jats:sup>-Regularization is incorporated to avoid the overfitting phenomenon and further optimizing our proposed model. Thirdly, transfer learning is introduced to enhance the feature representation and discrimination, which could boost the performance and robustness of the proposed model on small samples. Finally, various activation functions and dropout strategies are evaluated for further improving recognition performance. 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