The efficacy and effectiveness of any machine learning model are as good as the data set used to train the model in the first place. Under the current circumstances, although companies are producing more data than ever, the variety and diversity of the data available to train new models are still very limited. Therefore, the need of the hour is to cultivate data that provides adequate diversity to train machine learning models so that they become capable of handling newer challenges as the usage increases. But is that even possible? If so then what is the process that helps you achieve this goal? Let’s find out in this all-encompassing guide about machine learning data augmentation.
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What is Data Augmentation?
So, the answer to the above mentioned question must be pretty clear by now. Data Augmentation is the technique used by Deep learning practitioners to avail of diverse data sets that can be utilized to train efficient models. The primary need for any process like data augmentation is to ensure that a particular machine learning model is not just good during the training or testing phase, but also generalizes well for multiple use cases.
When we talk about the diversity of data, there are several syntactic and semantic layers in language that need to be available in order to claim that a data set is diverse enough. The process of data augmentation helps in increasing the size of the data set which in most cases is the prerequisite towards obtaining diverse data. Therefore, all-in-all data augmentation is a very crucial technique to ensure generalization besides overfitting and regularization to directly improve the chances of a model to become highly utilitarian.
From model prediction accuracy, reducing data overfitting, dealing with imbalance issues in classification, to reducing the expenditure on collecting data for training sets, data augmentation helps with everything.
How to Apply Data Augmentation
Computer vision applications rely heavily on data organization techniques to generate more data sets. For training their models, the techniques used for data augmentation fall under the categories of classic and advanced. Techniques for image augmentation using PyTorch are used for both image recognition and natural language processing. Geometric and color space augmentation techniques are the most popular ones and generate desirable results on most occasions. Commonly used techniques include:
Cropping is a simple concept where you just select a section of the image and then either zoom in or resize to create a new image. This way, you end up increasing the size of your available data set, thereby implementing data augmentation effectively.
Again, one of the most simplistic methods to implement data augmentation, you can just rotate the image 360 degrees or to whatever extent you desire, and eventually, you will get a new image out of the existing one.
A technique where the subject of the image is moved at various locations along the x axis and y axis respectively is called Translation. At whichever position you place the subject in the image, the neural network then examines it and captures the image to create and treat it as a new form of data.
Not just by changing the structure and orientation of the image, but also by retouching the lighting, luminescence, and color aspects, you can create a new image out of the existing ones. Adding contrast to images is one of the most important techniques of augmentation as it prepares your model for the varying amounts of lighting and exposure.
For Natural Language Processing Models
Easy Data Augmentation (EDA) Methods
The easy data augmentation technique is certainly justifying its name because users only have to make minor changes to obtain desired results. The mechanism of action is usually like changing a word in a sentence with its synonym so that the sentence appears as new, such that the model will perceive it as a unique entity. Again, this directly influences the generalization capability of the model so that it understands that the meaning conveyed by a sentence is the same even if newer words are incorporated at specific places. Random insertion, Random deletion, Text Substitution, etc., are good examples of Easy Data Augmentation (EDA) techniques.
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