Data augmentation

Data augmentation in data analysis are techniques used to increase the amount of data by adding slightly modified copies of already existing data or newly created synthetic data from existing data. It acts as a regularizer and helps reduce overfitting when training a machine learning model. It is closely related to oversampling in data analysis

See AI/Supervised Learning/Class imbalance

Resources

• https://en.wikipedia.org/wiki/Data_augmentation
• Tensorflow Data augmentation Tutorial
• Data Augmentation in Python: Everything You Need to Know
• https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html
• https://machinelearningmastery.com/how-to-configure-image-data-augmentation-when-training-deep-learning-neural-networks/

Code

• #CODE Deltapy - Tabular Data Augmentation
  • https://lschmiddey.github.io/fastpages_/2021/04/10/DeepLearning_TabularDataAugmentation.html
• #CODE Albumentations
  • https://albumentations.ai/
  • Albumentations is a computer vision tool that boosts the performance of deep AI/Deep learning/CNNs
• #CODE TSaug - A Python package for time series augmentation
  • tsaug.readthedocs.io
  • https://www.arundo.com/articles/tsaug-an-open-source-python-package-for-time-series-augmentation

References

• #PAPER A survey on Image Data Augmentation for Deep Learning (Shorten 2019)
• #PAPER AutoAugment: Learning Augmentation Policies from Data (Cubuk 2019)