Most machine learning and deep learning models rely heavily on data amount and variety to function well. The volume and diversity of data provided during training have a significant impact on the prediction accuracy of these models.
Deep learning models that have been taught to perform effectively on complicated tasks frequently include hidden neurons. The number of trainable parameters increases according to the number of hidden neurons.
The amount of data required is proportional to the number of model learnable parameters. One method for dealing with the difficulty of limited data is to apply a variety of transformations to the current data to synthesize new data.
The technique of synthesizing new data from existing data is referred to as ‘Data Augmentation.’ Data augmentation can be used to fulfill both requirements: the volume of data and the variety of the training data required to develop accurate machine learning or deep learning models.
In this post, we will look closely at data augmentation, its types, why it is essential, and much more.
So, what is Data Augmentation?
Data Augmentation is the process of developing new and representative data from existing data. You can accomplish this by including modified versions of existing data or synthesizing new data.
The datasets produced by this method will improve your machine learning or deep learning models by minimizing the risk of overfitting. It is the process of changing, or “augmenting,” a dataset with additional information.
This supplemental input might range from images to text, and it boosts the performance of machine learning systems.
Assume we want to build a model to categorize dog breeds and we have a big number of photographs of all varieties except pugs. As a result, the model would have difficulty categorizing pugs.
We could add additional (actual or false) pug photos to the collection, or we could double our current pug photographs (e.g. by replicating and distorting them to make them artificially unique).
What use does data augmentation serve in the present?
Applications for machine learning are rapidly developing and diversifying, especially in the field of deep learning. The challenges that the artificial intelligence industry faces can be overcome through data augmentation techniques.
Data augmentation can improve the performance and outcomes of machine learning models by adding new and diverse examples to training datasets.
When the dataset is large and sufficient, a machine learning model performs better and is more accurate. For machine learning models, data gathering and labeling may be time-consuming and expensive.
Companies can reduce their operational costs by changing datasets and utilizing data augmentation strategies.
Cleaning data is one of the stages in the development of a data model, and it is essential for high-accuracy models. However, the model won’t be able to anticipate proper inputs from the actual world if data cleansing decreases representability.
Machine learning models can be strengthened by employing data augmentation approaches, which produce variances that the model could encounter in the actual world.
Types of Data Augmentation
Real data augmentation
Real data augmentation occurs when you add genuine, supplementary data to a dataset. This can range from text files with additional attributes (for tagged pictures) to images of other objects comparable to the original object, or even recordings of the actual thing.
For example, by adding a few more features to an image file, a machine-learning model can detect the item more easily.
More metadata about each image (e.g., its name and description) might be included so that our AI model knows more about what each image represents before it begins training on those photos.
When it comes time to categorize fresh photos into one of our predetermined categories, like “cat” or “dog,” the model could be better able to detect the items that are present in an image and perform overall better as a result.
Synthetic Data Augmentation
Aside from adding more real data, you can also contribute synthetic data or artificial data that seems authentic.
This is beneficial for difficult tasks like neural style transfer, but it’s also good for any design, whether you’re using GANs (Generative Adversarial Networks), CNNs (Convolutional Neural Networks), or other deep neural network architectures.
For example, if we want to properly categorize pugs without having to go out and take a number of photos, we could add some false pug photographs to a collection of dog images.
This form of data augmentation is especially effective for enhancing model accuracy when collecting data is difficult, expensive, or time-consuming. In this situation, we are artificially expanding the dataset.
Assume that our initial group of 1000 dog breed photographs contains only 5 pug images. Rather than adding additional actual pug photographs from real dogs, let’s create a fake one by cloning one of the current ones and slightly distorting it so that it still seems like a pug.
Data Augmentation Techniques
Data augmentation approaches entail making little modifications to existing data. It’s the same as rephrasing a statement. We can divide data augmentation into three categories:
- Word Replacement: This data augmentation approach includes replacing current terms with synonyms. As an example, “This film is foolish” can become “This film is idiotic.”
- Sentence/Word Shuffling: This strategy involves switching the sequence of phrases or words while maintaining overall coherence.
- Syntax-Tree Manipulation: You change an existing sentence to be grammatically accurate while utilizing the same terms.
- Random Deletion: Although this strategy produces ugly writing, it is effective. As a result, the line “I will not purchase this record because it is scratched” becomes “I will not buy this because it is scratched.” The phrase is less clear, but it remains a plausible addition.
- Back Translation: This approach is both effective and enjoyable. Take a statement written in your language, translate it to another language, and then re-translate it back to your original language.
- Kernel Filters: This approach sharpens or blurs a picture.
- Image Combination: Although it may appear weird, you can mix photos.
- Erasing at Random: Delete a tiny portion of the current picture.
- Geometric Transformations: This approach comprises, among other things, arbitrarily flipping, rotating, cropping, or translating pictures.
- Flipping a picture: You can flip an image from horizontal to vertical orientation.
- Color Space Transformation: You can modify the RGB color channels or enhance any current color.
- Re-Scaling is the process of adjusting the visual scale. You have the option of scaling in or out. When you scale inward, the image becomes smaller than the initial size. The picture will be larger than the original if you scale it outward.
- Pitch: This approach involves changing the audio pitch.
- Change speed: Change the speed of the audio file or recording.
- More Noise: You can add more noise to the audio file.
Medical imaging is a prominent use case for data augmentation right now. Medical picture collections are small, and sharing data is difficult due to rules and privacy concerns.
Furthermore, data sets are much more constrained in the case of uncommon disorders. Medical imaging companies use data augmentation to diversify their data sets.
Scalability, diverse datasets, and relevance are some of the issues that need to be resolved in order to develop efficient data augmentation techniques.
In terms of scalability, augmented data has to be scalable so that many different models can use it. You’ll want to make sure that this can be duplicated for use in future models since setting up a data augmentation system that generates a big amount of pertinent, valuable, enhanced data can take some time.
In terms of heterogeneity, various datasets have distinct features that must be considered while developing augmented data. To develop appropriate enhanced data, the properties of each dataset must be utilized.
In other words, data augmentation will differ between datasets and use cases.
Finally, to guarantee that the advantages of the increased data exceed any hazards, the augmented data should be evaluated using suitable metrics before being utilized by machine learning models.
For example, the presence of significant background noise or unrelated items in image-based augmented data could have a detrimental impact on the performance of the model.
Ultimately, whether you’re attempting to forecast loss, identify financial fraud, or construct better image classification models, data augmentation is a critical way to build more accurate, robust models.
Through a superior training procedure, simple preprocessing and data augmentation can even assist teams in developing cutting-edge models.
Businesses can utilize data augmentation to reduce the amount of time spent preparing training data and to create machine learning models that are more accurate and more quickly.
By expanding the quantity of pertinent data in the dataset, data augmentation can also benefit machine learning models that already have a lot of data.