Get an in-depth analysis on what Data Augmentation is? What are the types & how it can be leveraged for developing deep learning models.
Today data is everything and it comes at a price. Not only is aggregating data a cost intensive process but it also requires tons of resources and time. Even upon doing that the chances of building a model that provides accurate results is skewed. It is for the same reason that data augmentation was devised to train machine learning and deep learning models. The data generated by using augmentation techniques aids in developing a system that can handle complex tasks, is robust, and can be scaled to the necessity.
There is a lot to uncover about the topic so to follow up read ahead…
The technique that is used to create new artificial data from already existing data sets is known as data augmentation. It is done by creating alteration or modification in the already existing copies of data that gives out data in the latent space which was previously not created.
This data is used as samples to create more accurate machine learning and deep learning models, and help them train better.
Aside from resource utilization, there are issues because of which data augmentation came into being. Providing accurate results is where the models often stutter. This leads to two issues which are:
A machine learning and deep learning model underfits when it is unable to pick up the underlying data within an image. The model seems to work well with the training data but performs poorly with the testing data.
There are several reasons for underfitting such as:
A model is overfit when it is not able to make correct predictions on the testing data. When a model is trained with lots of data, it starts to pick up data from the noise and incorrect data entries.
The reasons behind overfitting are:
Talking about the type of data augmentation and how it works then it can be classified into three different spectrums. These are:
Machines can’t distinguish images like humans. It is for the same reason image classification came into being. By using image classification, a machine is able to categorize an image into labels that are used to describe the data within. However, the success rate of a machine learning and deep learning model depends on its accuracy.
The models for image classification using data augmentation can be divided into three parts:
In a baseline model, the model is trained without data augmentation to establish a confusion matrix. This confusion matrix gives an overview of the data misclassified by the model, and the ones that were classified correctly.
For example, let’s say there are 1000 images of humans and monkeys, and we task the model to determine which is which. If the model is able to figure out 30 correct human images and 45 correct monkey images then this data can be used to establish a baseline model.
Augmented transformation is primarily carried out to remove classification errors. In this augmented data is pushed into the model for similar images having different attributes or transformations.
These can be classified in two broad categories:
It follows techniques mentioned below to create transformations:
The techniques used in this transformation are mentioned below:
During this step, the rate of datasets are kept the same to avoid overfitting. This is done to determine the change of accuracy with the augmented dataset.
This step is done to further enhance the accuracy of the model. Involving this step enables the model to reach accuracy numbers of up to 99% or more. Initially, a model primarily focuses on the center. It means, if images that have the subject that is either too small or is not centered will not be processed correctly.
Also adding to it, the idea behind is to create more difficulty for the model to predict the data. To perform this step, a same test image is transformed in 4 or 6 different datasets and fed to the system. The labeling of the object in the image is carried out taking the average of the value generated.
Data augmentation is a preferred method for improving the mapping of biological signals. The technique used for enhancing results of the accuracy related to EEG (electroencephalogram) or EEG classification. With EEG, it is possible to understand the electrical activity of the signals produced in a brain. While the use of synthetic data in the field is paramount, the data required to build a model to improve the results for BCI (Brain-Computer Interface) is scarce.
There are multiple reasons why acquiring data from EEG is not scalable enough to produce accurate results such as:
To achieve this, there are two techniques that are followed to generate more sample sets for the model. These are:
This technique applies different transformation methods such as cropping, flipping, rotation, scaling, etc.
These methods are not utilized for EEG because they are affected by the time-domain feature.
As the title suggests, it involves adding noise to the already available test datasets. It increases complexity for the model enabling it to make more accurate predictions. It also reduces overfitting of data.
There are several methods that are applied to generate more dataset for EEG using data augmentation. The use of these methods helps in creating a deep learning model that can handle complex tasks and the methodologies also reduces overfitting.
However, the most efficient models that are utilized for generating training sets are GANs (Generative Adversarial Networks) and (VAE) Variation Auto-Encoders. We will discuss it later on in the advance strategies.
Speech recognition is a commonly recognised technology for which data augmentation is used. Some great examples of this technology would be Alexa, Google Assistance, Siri, etc. While enough data is aggregated by each of the vendors providing these services, the deep learning speech recognition module struggles with issues like adequate volume, tonality, dialect, etc. To overcome these problems and let the model generalize the input, the speech recognition models are trained using augmented data.
Over the years, there have been multiple models that are created for generating augmented data for speech recognition. It is because the already available test datasets seem to overfit the model.
In this method, the signal of the audio is either squeezed or stretched using the waveform of the audio signal provided. This changes the sampling of the audio signals giving out a transformed dataset with modified frequencies.
The method works on the factor of x(t). The formula represents the time domain of the audio signal. Here a factor of “a” is added to the audio signal to change its frequency either to a high or low. On adding the factor, the formula looks like x(at). If the factor a>1 then it will create high frequency leading to a shortened audio signal and vice versa.
Instead of waveform, this method utilizes spectrograms. There are three strategies to pull of this method, which are:
The ideology behind the method is to create new scenarios for the audio signal’s waveform which are available in the test dataset.
It is quite similar to SpecAugment and also utilizes waveform. It uses two strategies, which are:
In this model, the deep learning model is fed with transformed data with pixel changes. These changes are made till the point where the model fails to correctly predict the accurate data. It is done to create a model that works perfectly fine during adversaries.
Unlike adversarial training, GANs is a generative model. It simply means that the model is capable of creating its own dataset that resembles the original dataset with slight variations. Using this technique can train the generator, the classifier, and the discriminator.
The intention behind this model is to fool the discriminator. The model tries to create real-like variations of the dataset that can’t be picked by the discriminator at least half the time. This way the augmented data created for any purpose be it image classification, signal processing, or speech recognition is perfect and real-like capable of training an accurate model to stay ahead of faulty predictions. The downside to this approach is that it requires tons of resources.
VAE is another generative model. In this model, autoencoder distributions are regularized. To generate new data, VAE makes sure that the dataset created is similar and has good properties in the latent space.
This model utilizes two aspects of an image i.e. the content and the style reference. To further create a blend of these aspects together to create an output image. To achieve this, the content image is optimized to match with similar content images to reach similar statistics. Also, the same step is followed for the style. Using both these data points, a blend is created that is used as augmented data for deep learning models.
This model takes the help of trial and error. Upon feeding the datasets and generating outputs, the model is rewarded in some way on making a correct decision and punished on making a bad one. A great example of understanding this model fundamentally is taking the analogy of dog training. Upon following the commands given to a dog, the trainer provides it with a treat and if the dog doesn’t listen to the command, no treat is given until it follows the command.
To understand the concept behind, one can also checkout the video mentioned below:
Using the methodologies of reinforced learning, augmented data is generated. Here reinforced learning provides the conditions for the classifier and trains them to provide accurate test datasets.
VisionERA is an intelligent document processing platform which means it can process unstructured documents without any difficulty. To achieve this status quo, the NLP algorithms designed for VisionERA had to undergo grueling training using data augmentation for deep learning. Not only is our platform capable of feats such as data extraction, validation, sorting, organizing, storage, etc but it can also pick up context from the documents at hand. Our platform VisionERA is also capable of autocorrecting incomplete data based on its training. And, the engine is designed such that it can accurately extract data from the documents giving out an accuracy of upto 99% or more. To develop such a versatile one-stop document processing solution data augmentation has played an important role.
Before data augmentation, organizations had to constantly tussle to find the relevant datasets. Also, the issues related to overfitting and underfitting were recurring events even upon huge investment of time, resources, and money. With data augmentation developers today are capable of creating models that run with their maximum efficiency. There are still multiple use cases for which there is constant research going to provide the most efficient solution to derive augmented data. While constant research is still running for the optimization of techniques devised that are already at hand. Yet, the ending note would be that yes machine learning and deep learning models of today are now more than capable. Also, the amount of automation we have achieved with modern day solutions is something to marvel at.
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