We introduce Deep Augmentation, an approach to data augmentation using dropout to dynamically transform a targeted layer within a neural network, with the option to use the stop-gradient operation, offering significant improvements in model performance and generalization. We demonstrate the efficacy of Deep Augmentation through extensive experiments on contrastive learning tasks in computer vision and NLP domains, where we observe substantial performance gains with ResNets and Transformers as the underlying models. Our experimentation reveals that targeting deeper layers with Deep Augmentation outperforms augmenting the input data, and the simple network- and data-agnostic nature of this approach enables its seamless integration into computer vision and NLP pipelines.

翻译：我们提出深度增强方法，这是一种利用dropout对神经网络目标层进行动态变换的数据增强技术，并可选择性使用梯度停止操作，从而显著提升模型性能与泛化能力。通过在计算机视觉和自然语言处理领域的对比学习任务中进行大量实验，我们验证了深度增强的有效性，实验发现以ResNets和Transformers作为基础模型时均能获得实质性性能提升。研究表明，对深层网络实施深度增强的效果优于对输入数据的增强，且该方法具有网络无关性与数据无关性的简洁特性，使其可无缝集成到计算机视觉和自然语言处理流程中。