Image Data Augmentation for Deep Learning: A Survey (2204.08610v2)

Abstract: Deep learning has achieved remarkable results in many computer vision tasks. Deep neural networks typically rely on large amounts of training data to avoid overfitting. However, labeled data for real-world applications may be limited. By improving the quantity and diversity of training data, data augmentation has become an inevitable part of deep learning model training with image data. As an effective way to improve the sufficiency and diversity of training data, data augmentation has become a necessary part of successful application of deep learning models on image data. In this paper, we systematically review different image data augmentation methods. We propose a taxonomy of reviewed methods and present the strengths and limitations of these methods. We also conduct extensive experiments with various data augmentation methods on three typical computer vision tasks, including semantic segmentation, image classification and object detection. Finally, we discuss current challenges faced by data augmentation and future research directions to put forward some useful research guidance.

Image Data Augmentation for Deep Learning: A Survey

This paper, authored by Suorong Yang et al., presents a comprehensive survey on various image data augmentation methods that play a crucial role in the field of deep learning, particularly for computer vision (CV) tasks. Recognizing the dependency of deep neural networks on extensive labeled datasets to avoid overfitting, the authors address a fundamental issue in real-world applications: the scarcity of labeled data. Data augmentation, by enhancing the volume and diversity of training data, has become an indispensable strategy in training deep learning models effectively.

Overview of Methods

The paper proposes a taxonomy of image data augmentation techniques, categorizing them into basic and advanced methods:

1. Basic Data Augmentation Methods:
   • Image Manipulation: Techniques such as rotation, flipping, scaling, and noise injection aim at simple transformations within the input space.
   • Image Erasing: Strategies like Cutout, Hide-and-Seek (HaS), and Random Erasing focus on occluding parts of the image to improve neural network robustness.
   • Image Mix: Methods involving the synthesis of two or more images, such as Mixup and CutMix, help in linking diverse dataset characteristics.
2. Advanced Approaches:
   • Auto Augment: Automated searching algorithms, like AutoAugment and Fast AutoAugment, leverage reinforcement learning to discover effective augmentation strategies across different datasets.
   • Feature Augmentation: This involves exploiting transformations within learned feature spaces as demonstrated by FeatMatch.
   • Deep Generative Models: The use of GANs and its variants, such as CycleGAN and StarGAN, facilitates realistic data generation simulating natural distributions.

Experimental Findings

Empirical results across three CV tasks—semantic segmentation, image classification, and object detection—validate the efficacy of these augmentation methods. The authors use datasets like PASCAL VOC, CIFAR-10/100, and COCO2017 to demonstrate tangible improvements in task-specific metrics. For instance, data augmentation led to notable improvements in mean IoU for segmentation and accuracy for classification tasks across multiple deep learning models.

Implications and Future Directions

The exploration of data augmentation methods holds significant implications for both practical application and theoretical advancements in deep learning:

• Practical Significance: As models advance, the necessity for diverse and voluminous datasets underscores the importance of mastering augmentation techniques. Effective data augmentation might alleviate the expenses tied to data acquisition and labeling, a hurdle in scaling intelligent systems.
• Theoretical Advancement: There exists an evident gap in theoretical understanding regarding the effect of augmentation techniques on learned models. Future research should focus on establishing a concrete theoretical framework to assess and predict the impact of data augmentation.
• Challenges and Open Questions:
  • The paper identifies several challenges such as the lack of theoretical understanding of augmentation strategies and determining optimal dataset size.
  • Evaluating the synthetic data’s quality without relying on task-specific metrics remains an open issue.
  • The combinatorial use of different augmentation methods across varying datasets is underexplored and warrants further inquiry.

In conclusion, this survey serves as a pivotal resource for researchers engaged in the enhancement of deep learning models through data augmentation. It provides a detailed classification of augmentation techniques, backed by empirical evidence and poised to guide both current applications and future innovations in deep AI systems.