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Data Augmentation – Everything to Know

What Is Data Augmentation?

Data augmentation is the process of artificially increasing the amount of data from existing datasets.

This technique is seeing increased adoption in image classification and NLP machine learning algorithms.

Image classifying ML algorithms require a large dataset containing various images. However, in many cases, the datasets available are very limited which can lead to “data overfitting”.

(Data overfitting is a statistical error resulting from limited datasets).

Blurring, rotating, and padding images via data augmentation enables the creation of more images from an existing dataset.

Data Augmentation and AI Training

Data augmentation is part of the Alternative AI Training Datasets trend.

Training AI models is getting more expensive, largely due to expenses related to obtaining robust datasets.

For many startups and other smaller institutions, the cost of gathering real-life AI datasets often exceeds their entire computing budgets.

This has created the need to find alternative ways to generate the datasets required to train AI models.

Synthetic data is an example of a trending technique used to generate cheaper datasets.

In fact, Gartner forecasts that by 2030, synthetic data will become the primary data source used to train AI models.

Related: Generative Adversarial Network (GAN)

Data Augmentation Techniques

There are various ways to perform data augmentation. Some of the most popular methods are:

1) Random Crop

This technique involves randomly cropping images from the dataset to create new images.

2) Random Flip

This technique entails randomly flipping images horizontally or vertically to create new images.

3) Random Rotation

This technique consists of randomly rotating images to create new images.

4) Adding Noise

This technique adds random noise to images to create new images.

5) Image Stitching

This technique stitches together multiple images to create new composite images.

6) Generative Adversarial Networks (GANs)

This technique uses a GAN to generate new images from scratch.

Data Augmentation explained

Data Augmentation Tools and Libraries

There are various tools and libraries that can be used for data augmentation.

Some of the most popular ones are:

1) TensorFlow Image Data Pipeline

2) OpenCV

3) imgaug

4) Albumentations

5) Keras Preprocessing Layers

6) torchvision.transforms

7) Augeas

8) imgaug-doc

9) mxnet.image

10) torchvision

11) PILLOW-SIMD

12) imgaug-gitpython

13) Prefect

14) Scikit-image

15) SimpleITK

16) Open3D

17) DALI

18) NVIDIA DALI plugins for Apache MXNet and PyTorch

19) GANBreeder

20) Data Augmentation API by Roboflow.ai

FAQs – Data Augmentation

What is data augmentation in machine learning?

Data augmentation is a technique used to artificially increase the amount of data from existing datasets.

This technique is seeing increased adoption in image classification and NLP machine learning algorithms.

What are some common methods of data augmentation?

Some of the most popular methods of data augmentation include: random crop, random flip, random rotation, adding noise, image stitching, and generative adversarial networks (GANs).

What are some common tools and libraries used for data augmentation?

Some of the most popular tools and libraries used for data augmentation include: TensorFlow Image Data Pipeline, OpenCV, imgaug, Albumentations, Keras Preprocessing Layers, torchvision.transforms, Augeas, imgaug-doc, mxnet.image, torchvision, PILLOW-SIMD, imgaug-gitpython, Prefect, Scikit-image, SimpleITK, Open3D, DALI, NVIDIA DALI plugins for Apache MXNet and PyTorch, GANBreeder, and Data Augmentation API by Roboflow.ai.

How does data augmentation help to avoid overfitting?

Data augmentation can help to avoid overfitting by artificially increasing the amount of data from existing datasets.

This technique is particularly useful in image classification and NLP machine learning algorithms where datasets are often limited.

Conclusion – Data Augmentation

Data augmentation is a critical technique for training image classification and NLP machine learning algorithms.

This technique artificially increases the amount of data from existing datasets which can lead to improved model performance.

There are various methods and tools that can be used for data augmentation. Some of the most popular ones are listed above.

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