What is Data Augmentation?

The process of creating updated copies of a dataset using pre-existing data is known as data augmentation, and it artificially expands the training set. It comprises adding minor changes to the dataset or using deep learning to create new data points.

When talking about data augmentation, it is mandatory that we understand a thing or two about CNNs.

Convolutionary Neural Networks(CNNs) are a type of deep learning architecture used in computer vision. CNNs are an important component in data augmentation. CNNs classify objects accurately in different orientations, and this is the key concept in data augmentation.

In our last wiki, we talked about synthetic data in diffusion models. There is a difference between synthetic data and data augmentation.

To be fair, both are generative techniques that augment an existing dataset to enhance machine-learning model performance.

Automatically generated wholly fabricated data is referred to as synthetic data. One example is training an object identification model with computer-generated photos instead of real-world data.

Data augmentation, on the other hand, creates duplicates of already-existing data and modifies them to provide more variety and volume to a given set. It uses different techniques, such as flipping, scaling, cropping, contrast, adding noise, and others, to augment the existing dataset.

Sometimes, in the real world, you have data, but it is limited. You have to train the model with different variations of the data; at that time, data augmentation helps create a diverse variety of data. Let us understand it with a simple example -

You are creating an AI model that can identify different animals. You are provided with a dataset of 500 images. A robust model can identify the data in any condition. To train the model, you need a more diverse dataset.

Suppose you have ten images of giraffes. If the Giraffe is looking right in all 10 of them, the model will be trained to believe that the yellow-colored creature looking right is a Giraffe.

When input with a left-looking Giraffe, the model will not identify it, whereas, in reality, it is a Giraffe.

If data augmentation techniques like rotating, flipping, zooming, cropping, or others are implemented on the data set, adding invariance to the Giraffe images. This will create a much more diverse dataset to train the model.

Some benefits of data augmentation are -

Augmented data enables the model to learn from a wider range of samples. This makes the model more robust in identifying the samples.

When the training set is more diverse, the model is more likely to generalize to new and unseen data. It prepares the model for unpredictability.

Gathering new datasets requires more money, hampering the wallet. Creating a more diverse dataset with existing data is a cost-effective and smart use of resources. It drastically reduces the cost of the collection of new data.

The real-world applicability of models trained on augmented data is generally enhanced since they exhibit greater resilience to perturbations in the data.

Depending on the type of data and the changes made, data augmentation techniques fall into various categories - a few of the most popular data augmentation areas are -

Generative AI is crucial to data augmentation because it makes the creation of synthetic data easier. It facilitates the production of realistic data more quickly, protects data privacy, and broadens the diversity of data.

Two opposing core neural networks make up the framework of generative adversarial networks or GANs. The discriminator then distinguishes between the real and artificial data samples that the generator produced.

Because GANs concentrate on tricking the discriminator, they gradually increase the generator's output. Data augmentation with extremely dependable samples that closely resemble the original data distribution is possible with data that can mislead the discriminator, which qualifies as high-quality synthetic data.

A variational autoencoder (VAE) is a type of neural network that can help reduce the need for laborious data collection and increase the sample size of core data. A decoder and an encoder are the two networks that are coupled in VAEs. Sample images are fed into the encoder, which converts them into an intermediate form. Using its understanding of the original samples, the decoder takes the representation and uses it to build similar images. Because VAEs can produce data that is very similar to sample data, they can be used to provide diversity while preserving the original distribution of the data.

We have seen that data augmentation helps in creating diverse datasets. This smart use of data finds its use in many industries. Here are some examples -

Data augmentation is a helpful tool in medical imaging since it enhances diagnostic models that use images to identify, classify, and diagnose diseases. Creating an enhanced image obtains more training data for models, particularly for rare diseases where source data variances are absent. Synthetic patient data is created and used in a way that respects all data privacy concerns and promotes medical research.

We have seen models used to display products in the retail industry. Here, data augmentation can hugely impact the recognition and classification of products according to visual cues. Through the process of data augmentation, product photos can be artificially varied, resulting in a training set with greater variation in terms of lighting, image backdrops, and product angles.

Augmentation creates artificial instances of fraud, making it possible for algorithms to be trained to identify fraud more precisely in real-world situations. Larger training data pools that aid in risk assessment scenarios enhance deep learning models' potential to assess risk effectively and forecast future trends.

Text data augmentation is often employed when performance metrics need to be improved, and there is a lack of high-quality data. You can use random insertion and deletion, word embedding, synonym augmentation, and character swapping. Low-resource languages can also benefit from these strategies.

Data Augmentation is proving to be helpful in situations where collecting large datasets is not possible. Healthcare, retail, and many other sectors are seeing the growing use of data augmentation.

Although your data and application must be carefully considered before integrating data augmentation into your process, the advantages greatly exceed the difficulties. Leveraging the full potential of generative AI will require remaining up to date on the newest methods and trends in data augmentation as the field develops.