The global market for big data is projected to be 103 billion dollars by 2027 according to Statista. Using big data to create models is essential for businesses and organizations, and data augmentation techniques are needed to optimize predictive models. Collecting enough data to build accurate models is the crux of machine learning, so using data augmentation methods is an important step to harness the power of big data.
This article discusses a wide range of data augmentation techniques and how to implement them. Both deep learning and machine learning rely on data augmentation ideas. If you have been wondering how to learn data augmentation, start here. We’ll cover the basics of different data augmentation methods.
Best Data Augmentation Technique Examples
Data augmentation techniques increase the size of a training dataset used to train a deep learning model. Smart augmentation methods include flexible image augmentations and geometric augmentations to create custom image augmentation libraries. Image augmentation techniques, like reinforcement learning, can be applied to computer vision, deep learning, and machine learning.
This image data augmentation technique can create unique images for a training dataset, which can be used to train a machine learning model to perform digit recognition tasks. Rotation is a geometric transformation that spins an image between one degree and 359 degrees and gives the new image a new data label. You now have many new images that are slightly different from the original image to create a larger training dataset.
Flipping is another geometric transformation that rearranges the pixels of an image while maintaining the initial pixel values. Horizontal flips work for a wide range of images, while vertical flips may be more suitable for deep learning models. Some frameworks may not allow for vertical flips, but you can achieve similar results by rotating an image 180 degrees and flipping it horizontally.
This process involves resizing a section of an image, also known as random cropping. Cropped images are a common image transformation. Random cropping is similar to translations, but random cropping reduces the input size of the image while translations preserve the original image’s dimensions.
This augmentation tool alters the degree of separation between an image’s darkest and brightest areas. This image transformation operation creates a new image with distinct colors and brightness values by changing the contrast of the original image. These color space transformations can create a robust deep learning library.
Easy Data Augmentation Operations
Easy Data Augmentation (EDA) operations are used for text augmentation and aid in machine learning. Standard EDA operations include random swaps, synonym replacement, text substitution, and random insertion. Random deletion and word and sentence shuffling are also part of text transformations. Deep learning frameworks use EDA operations to increase classification accuracy.
Noise injection is a basic type of augmentation strategy that is used to create a deep learning library to train neural networks. This augmentation method takes real-life images from an original dataset and produces blurry patches and background noise. When you use an image augmentation library to train a deep learning model on classification tasks, the model might not get enough variance in the training process.
Gaussian noise is a specific type of noise injection used to build custom image augmentation libraries from limited original datasets. Images in real life are messy and variable, so you need to give the model images background noise to improve model performance. Gaussian noise introduces small random transformations in the main object of an image, while salt and pepper noise puts random irregularities all over the image.
Generative Adversarial Network (GAN)
This sophisticated augmentation technique learns patterns from an original dataset to build a better image augmentation library and improve machine learning. GANs use learned patterns to create new images to improve deep learning libraries.
Data Augmentation Ideas: Top 5 Tips to Master Data Augmentation
Knowing data augmentation strategies isn’t enough to become an expert in sophisticated augmentation functions and techniques. There are plenty of ways to master a range of augmentation techniques, from taking online tech courses to practicing with popular datasets. We’ve listed a few tips below.
Take Data Augmentation Classes
Online classes are a great way to learn. In fact, according to Statista, 41 percent of college students prefer online learning. Data augmentation courses will teach you about data augmentation techniques and assign practical exercises to better understand how deep learning models work.
Check Augmented Images
A smart augmentation policy is to spot-check images in your new image augmentation library to make sure you are creating proper augmentations. It’s easy to overlook errors when creating a complex augmentation pipeline, but a bad image dataset will build a bad classification model.
Use the Right Data Augmentation Method for the Task
Choosing the right data augmentation strategy gives you finer control over the neural network training process and improves model performance. If you are trying to build a neural network for image recognition vision tasks with an image dataset full of symmetrical images, flipping won’t create a robust deep learning library. A better augmentation technique in that example would be random cropping.
Use a Few Data Augmentation Techniques at a Time
When working on a custom image augmentation library, don’t use too many augmentation functions simultaneously. If you use too many image transformation operations on a single image, you could end up with distorted images that don’t improve the learning rate of the model instead of proper augmentations.
Monitor the Augmentation Process
While running smart augmentation processes or custom augmentations, you should log your computer’s total CPU consumption, memory consumption, and error outputs to determine the process’ efficiency. If you are running a complex augmentation pipeline, you might need to simplify a few steps to make the process more efficient.
Are Data Augmentation Techniques Worth Learning?
Data augmentation techniques are worth learning because they are essential for processing big data to use in deep learning frameworks that improve human lives. For example, data scientists use image augmentation techniques on medical images to improve skin lesion classification. Computer and information research scientists make an average salary of $131,490 to do this important work.
Data Augmentation Techniques FAQ
Yes, data augmentation improves accuracy if you choose the right augmentation strategies to apply to the dataset. Augmented data gives you a more comprehensive training dataset, but you have to use the right augmentation process to improve the learning rate of a classification model. The right geometric transformation can help with image classification tasks and improve classification accuracy.
The main disadvantage of deep learning operations is that it requires a huge amount of data to perform optimally, and creating custom augmentation libraries for each classification model is a lot of work. Most deep learning operations require more than a single augmentation technique to transform raw pixels into a robust deep learning library.
No, proper data augmentation does not cause overfitting, but rather prevents overfitting. However, some combinations of augmentation tools cause data underfitting in deep learning models. Albumentations and transforms are two vision tools that can improve a classification model.
Albumentations is a data augmentation tool used to create custom augmentation libraries. Albumentations has a set of functional transforms that you can use to make custom augmentation libraries to improve the classification accuracy of a convolutional network.
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