Mixed sample augmentation (MSA) has witnessed great success in the research area of semi-supervised learning (SSL) and is performed by mixing two training samples as an augmentation strategy to effectively smooth the training space. Following the insights on the efficacy of cut-mix in particular, we propose FMixCut, an MSA that combines Fourier space-based data mixing (FMix) and the proposed Fourier space-based data cutting (FCut) for labeled and unlabeled data augmentation. Specifically, for the SSL task, our approach first generates soft pseudo-labels using the model’s previous predictions. The model is then trained to penalize the outputs of the FMix-generated samples so that they are consistent with their mixed soft pseudo-labels. In addition, we propose to use FCut, a new Cutout-based data augmentation strategy that adopts the two masked sample pairs from FMix for weighted cross-entropy minimization. Furthermore, by implementing two regularization techniques, namely, batch label distribution entropy maximization and sample confidence entropy minimization, we further boost the training efficiency. Finally, we introduce a dynamic labeled–unlabeled data mixing (DDM) strategy to further accelerate the convergence of the model. Combining the above process, we finally call our SSL approach as ”FMixCutMatch”, in short FMCmatch. As a result, the proposed FMCmatch achieves state-of-the-art performance on CIFAR-10/100, SVHN and Mini-Imagenet across a variety of SSL conditions with the CNN-13, WRN-28-2 and ResNet-18 networks. In particular, our method achieves a 4.54% test error on CIFAR-10 with 4K labels under the CNN-13 and a 41.25% Top-1 test error on Mini-Imagenet with 10K labels under the ResNet-18. Our codes for reproducing these results are publicly available at https://github.com/biuyq/FMixCutMatch.

混合样本增强（MSA）在半监督学习（SSL）的研究领域中取得了巨大的成功，它通过将两个训练样本混合为一种增强策略来有效地平滑训练空间。尤其是在对剪切混合效果的见解之后，我们提出了FMixCut，这是一种MSA，将傅立叶空基数据混合（FMix）和提议的傅立叶空基数据剪切（FCut）相结合，用于标记和未标记的数据增强。具体来说，对于SSL任务，我们的方法首先使用模型的先前预测生成软伪标签。然后训练该模型以惩罚FMix生成的样本的输出，以使其与混合的伪伪标记一致。另外，我们建议使用FCut，一种新的基于Cutout的数据增强策略，该策略采用FMix的两个蒙版样本对进行加权交叉熵最小化。此外，通过实施两种正则化技术，即批标签分配熵最大化和样本置信度熵最小化，我们进一步提高了训练效率。最后，我们引入了动态的标记-非标记数据混合（DDM）策略，以进一步加速模型的收敛。结合以上过程，我们最终将我们的SSL方法称为“ FMixCutMatch”，简称为FMCmatch。结果，建议的FMCmatch通过CNN-13，WRN-28-2和ResNet-18网络在各种SSL条件下在CIFAR-10 / 100，SVHN和Mini-Imagenet上实现了最新的性能。特别地，我们的方法达到了4。在CNN-13下带有4K标签的CIFAR-10上有54％的测试错误，在ResNet-18下带有10K标签的Mini-Imagenet上有41.25％的Top-1测试错误。我们用于复制这些结果的代码可从https://github.com/biuyq/FMixCutMatch公开获得。