What is Data Augmentation?

Data augmentation refers to a process to increase the training data set by creating new but realistic data. For this purpose, various techniques are used to generate artificial data points from existing ones.

What is Data Augmentation?

In many use cases, the problem is that the data set is simply not large enough for deep learning algorithms. However, especially in the area of supervised learning, collecting new data points is not only expensive but may not even be possible.

However, if we simply create new data sets, we can also quickly run into a problem with overfitting. For this reason, there are several techniques in the field of data augmentation that ensure that the model can actually learn new relationships from artificially created data points.

In which areas are small data sets a problem?

In recent years, many new Machine Learning and especially deep learning architectures have been introduced that promise exceptional performance in various application fields. However, all these architectures have in common that they have many parameters to be learned. Modern neural networks in image or text processing quickly have several million parameters. However, if the data set has only a few hundred data points, these are simply not enough to train the network sufficiently and therefore lead to overfitting. This means that the model merely learns the data set by heart and thus generalizes poorly, delivering only poor results for new data.

However, especially in these areas, i.e. image and text processing, it is difficult and expensive to obtain large data sets. For example, there are comparatively few images that have concrete labels, such as whether there is a dog or a cat in them. In addition, data augmentation can also improve the performance of the model, even if the size of the dataset is not a problem. This is because by changing the data set, the model becomes more robust and also learns from data that differs in structure from the actual data set.

What is the purpose of Data Augmentation?

Data augmentation plays a pivotal role in data science and machine learning by aiming to enhance the training data. Its primary purpose is twofold: to expand the dataset’s effective size and to improve model performance.

One key objective is to bolster the dataset size. In many instances, obtaining an extensive, labeled dataset proves challenging and costly. Data augmentation bridges this gap by creating additional training samples, contributing to more robust models.

Another critical purpose is to boost model generalization. By exposing the model to various transformations of the same data point, it learns to adapt to different scenarios. This results in better performance on unseen or real-world data.

Additionally, data augmentation helps mitigate overfitting, particularly when the model becomes too specialized in capturing training data noise. It balances class imbalances, enhances model robustness to data variability, and encourages adaptability to diverse conditions.

Ultimately, data augmentation serves to create more realistic, diverse training data, reduce annotation efforts, and improve the cost-efficiency of model development. It empowers models to better handle real-world challenges and deliver more accurate predictions.

What methods are there to change data?

Depending on the application area, there are different methods to create new data points and thereby artificially extend the data set. For this purpose, we consider the areas of image, text, and audio processing.

Image Processing

There are various use cases in which Machine Learning models receive images as input, for example for image classification or the creation of new images. The data sets in this area are usually relatively small and can only be expanded with great effort.

Therefore, in the field of data augmentation, there are many ways to increase a small image dataset many times over. When working with images, these methods are also particularly effective as they add new views to the model, making it more robust. The most common methods in this area include:

Geometric changes: A given image can be rotated, flipped, or cut to create a new data point from a given one.
Color changes: By changing the brightness or colors (e.g. black and white) a given image can be changed without changing the label, e.g. “there is a dog in the image”.
Resolution change: Sharp images can be selectively blurred to train a robust model that can reliably perform the task even with worse input data.
Other methods: In addition, there are other methods for modifying images that are significantly more involved. For example, random parts of the image can be deleted or multiple images with the same label can be mixed.

Text Processing

While there is a large amount of text that we can also generate ourselves via scraping websites, for example, these are usually not labeled and thus cannot be used for supervised learning. Again, there are some easier and harder methods to create new data points. These include:

Translation: Translating a sentence into another language and then translating it back into the original language yields new sentences and words, but still the same content.
Reordering sentences: The arrangement of sentences in a paragraph can be changed without affecting the content.
Replacement of words: Words can also be selectively replaced with their synonyms to teach the model a broad vocabulary and thus become more robust.

Audio Processing

Data augmentation can also be applied to the processing of audio files. In fact, in this field, there are similar problems as in text processing, since data for supervised learning is very rare. Therefore, the following techniques for modifying data sets have become widely accepted:

Adding noise: By incorporating noise in the form of interfering sounds, for example, data points can be multiplied and the data set thus increased.

Audio Augmentation — Adding Background Noise | Source: PyTorch

Changing the speed: By increasing or decreasing the playback speed, the machine learning models become more robust and can also handle faster-spoken passages well.

What are the Advantages and Disadvantages of Data Augmentation?

Modification of data sets in the field of Data Augmentation can have advantages and disadvantages in the application.

On the one hand, high-performance models are getting bigger and bigger these days and more parameters have to be trained. This quickly leads to the risk that the data set is too small and the model is overfitted, i.e. it learns the properties of the training data set by heart and reacts poorly to new data. This problem can be solved by data augmentation. Furthermore, in various use cases, large datasets, especially in supervised learning, can only be obtained with high resource input. Thus, artificial augmentation of the data set is advantageous.

Apart from the data set size, data augmentation often leads to more robust models that can still deliver good results even with unclean data, for example with noise or poor quality. In reality, data quality issues can sometimes occur, so robust models are always beneficial.

On the other hand, data augmentation means another step in training a model. This can cost not only time but also resources if memory-intensive data, such as images or videos, have to be processed. In addition, biases are inherited from the old data set, which makes ensuring a fair data set all the more important.

Which libraries and tools can be used for Data Augmentation?

In the world of data augmentation, there is a wide range of libraries and tools available that make the process of augmenting data more efficient and convenient. These libraries offer various pre-built transformations and customization options, catering to different data types and domains. Here’s a look at some popular data augmentation libraries and tools:

Augmentor:

Augmentor is a versatile Python library specifically designed for image data augmentation. It allows you to easily apply operations like rotation, flipping, and resizing to images.
Features include a simple API, batch processing, and the ability to chain multiple augmentations together.
Augmentor is an excellent choice for enhancing image datasets for computer vision tasks.

ImageDataGenerator (Keras):

If you’re working with deep learning models in Keras, the ImageDataGenerator class is a powerful tool for image augmentation.
It provides a wide array of image transformations, including rotation, zooming, and horizontal/vertical flipping.
Integrated seamlessly with Keras, it simplifies the process of augmenting image data for training neural networks.

NLTK (Natural Language Toolkit):

NLTK is a comprehensive library for natural language processing. It includes various text augmentation techniques for NLP tasks.
Features such as synonym replacement, word insertion, and sentence paraphrasing are available for generating augmented text data.
NLTK is a valuable resource for enhancing text datasets in NLP applications.

OpenCV:

OpenCV (Open Source Computer Vision Library) is a versatile computer vision library that includes functionalities for image augmentation.
It offers a wide range of image processing functions like rotation, scaling, and color manipulation, making it suitable for both basic and advanced image augmentation needs.
OpenCV is often used for data augmentation when working with image data in computer vision projects.

TensorFlow Data Augmentation Layers:

TensorFlow, a popular deep learning framework, provides data augmentation layers as part of its Keras API.
These layers can be added directly to your neural network architecture, allowing for on-the-fly data augmentation during model training.
This approach ensures that the model sees diverse training data without the need for preprocessing steps.

Librosa:

Librosa is a Python library for audio and music analysis. It offers functionalities for audio data augmentation.
Techniques such as time stretching, pitch shifting, and noise addition are available for augmenting audio datasets.
Librosa is commonly used in speech recognition and audio classification tasks.

Albumentations:

Albumentations is a library designed for advanced image augmentation, particularly for computer vision tasks.
It is known for its speed and flexibility, offering a wide range of augmentations and the ability to work with different image formats.
Albumentations is popular among practitioners working on image segmentation and object detection.

Custom Scripts and Pipelines:

Depending on your specific data and requirements, you can create custom data augmentation scripts and pipelines using libraries like NumPy and scikit-image.
Custom scripts provide flexibility in designing unique augmentation techniques tailored to your data.

When choosing a data augmentation library or tool, consider the type of data you’re working with and the specific requirements of your project. These libraries can significantly streamline the data augmentation process, allowing you to generate more diverse and augmented datasets for improved model training and performance.

This is what you should take with you

Data augmentation is a method of increasing the size of a data set by adding modified data points again.
This can increase the size of a dataset and make the trained model more robust as it can handle different variations of the dataset.
Data augmentation can be applied in image, text, or even audio processing.