Why Data Augmentation? Modern machine learning models, such as deep neural networks, may have billions of parameters and require massive labeled training datasets—which are often not available. The technique of artificially expanding labeled training datasets—known as data augmentation—has quickly become critical for combating this data scarcity problem. Today, data augmentation is used as a secret sauce in nearly every state-of-the-art model for image classification, and is becoming increasingly common in other modalities such as natural language understanding as well. The goal of this blog post is to provide an overview of recent efforts in this exciting research area.
The Open Challenges in Data Augmentation Practical Methods of Learnable Data Augmentations Theoretical Understanding of Data Augmentations New Direction: Data Augmentations for Model Patching Further Reading Automating the Art of Data Augmentation (Part I: Overview) Automating the Art of Data Augmentation (Part II: Practical Methods) Automating the Art of Data Augmentation (Part III: Theory) Automating the Art of Data Augmentation (Part IV: New Direction) 链接地址： https://ai.stanford.edu/blog/data-augmentation/