论文标题
您只切一次：单剪地增强数据的增强
You Only Cut Once: Boosting Data Augmentation with a Single Cut
论文作者
论文摘要
我们向您展示一次（YOCO）进行数据增强。 Yoco将一张图像切成两片，并在每件零件中单独执行数据增强。应用YOCO改善了每个样本增强的多样性，并鼓励神经网络从部分信息中识别对象。 Yoco享受无参数，轻松使用的属性，并免费提供几乎所有的增强。进行了彻底的实验以评估其有效性。我们首先证明Yoco可以无缝地应用于不同的数据增强，神经网络体系结构，并带来CIFAR和Imagenet分类任务的性能增长，有时会超过传统的图像级增强。此外，我们向YOCO益处进行了对比的预培训，以更强大的表示，可以更好地将其转移到多个下游任务中。最后，我们研究了Yoco的许多变体，并经验分析了各个设置的性能。代码可在GitHub上找到。
We present You Only Cut Once (YOCO) for performing data augmentations. YOCO cuts one image into two pieces and performs data augmentations individually within each piece. Applying YOCO improves the diversity of the augmentation per sample and encourages neural networks to recognize objects from partial information. YOCO enjoys the properties of parameter-free, easy usage, and boosting almost all augmentations for free. Thorough experiments are conducted to evaluate its effectiveness. We first demonstrate that YOCO can be seamlessly applied to varying data augmentations, neural network architectures, and brings performance gains on CIFAR and ImageNet classification tasks, sometimes surpassing conventional image-level augmentation by large margins. Moreover, we show YOCO benefits contrastive pre-training toward a more powerful representation that can be better transferred to multiple downstream tasks. Finally, we study a number of variants of YOCO and empirically analyze the performance for respective settings. Code is available at GitHub.
