[Kaggle Study] 11. Data Augmentation

• Deep learning fundamentally requires a large amount of data for effective training.
• Additionally, to solve the chronic problem of overfitting in deep learning, it needs sufficient high-quality data.
• However, increasing the amount of data requires significant cost and time, and in some cases, it can be difficult to even collect or process the data.
• To address this issue, various Data Augmentation techniques have been developed to create new data using existing data.
• While there are various augmentation methods depending on the type of data, we will only cover those related to image data.

Basic image manipulation	Deep learning approach	Meta learning
Geometric Transformation	Adversarial training	Neural Augmentation
Color Space Transformation	GAN Data Augmentation	Auto augmentation
Mixing Images	Neural Style Transfer	Smart augmentation
Random Erasing
Kernel Filters

• While there are various methods of data augmentation, there are important principles to follow.
• Semantically Invariant Transformation means that augmentation should be performed while preserving the important aspects of the data.

#1 Basic Image Manipulation

• Geometric Transformation is a method of creating new images by applying Crop, Rotate, Contrast, Invert, and Flip operations to existing images.

• Color Space Transformation is a method of creating new images by adjusting the RGB values of existing images.
• Mixing Images is a technique that performs Weighted Linear Interpolation between two images using a λ value between 0 and 1, where the label is also assigned proportionally to the λ value.

 

• Random Erasing creates new images by erasing random areas of the image.
• We can also combine Basic image manipulation methods:
  • Cutmix combines Mixing images technique and Random erasing technique.
    • It works by drawing a box on image A and erasing it, then filling that empty area with a patch extracted from image B. 
  • PuzzleMix is an improved version of CutMix.
    • It mixes two images while preserving important features from both images.

#2 Deep learning approach

• Adversarial training:
  • Adversarial attack refers to presenting intentionally manipulated input values (adversarial examples) to the training model to make the DNN produce incorrect results.
  • Adversarial training is a learning method that creates multiple adversarial examples, presents them to the model, identifies under what circumstances the model makes misclassifications, and then modifies the model to improve overall performance.
• GAN Data Augmentation:
  • Uses GAN (Generative Adversarial Networks) models to generate samples similar to existing data to increase the amount of data. 

#3 Meta Learning

• Autoaugmentation
  • Among numerous data augmentation methods, AutoAugmentation is a model that suggests techniques suitable for a given dataset.
  • Google implements AutoAugmentation by training with PPO (Proximal Policy Optimization) to find the optimal combination among 16 commonly used data augmentation techniques.
  • However, it requires extensive computational resources, has a large search space, takes a very long time, and can only be used in limited environments.
  • Several methods have been proposed to improve the calculation speed of AutoAugmentation.
  • Examples include Population Based Augmentation, Fast AutoAugment, and Faster AutoAugment.
• RandAugmentation
  • While previous techniques focused on finding suitable augmentation methods, RandAugmentation doesn't seek a specific model but instead randomly selects and applies augmentation methods for each batch.
  • While its performance is similar to other models, it has the advantage of having simpler code.

 

Reference

1) Data Augmentation

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