Machine Learning is extremely popular these days, and more innovation-minded industries are turning to the field. However, machine learning works only as well as the quality of the data it uses. Consequently, it’s essential to provide as many data enhancements as is practical.
This need for new data enhancements is where data augmentation comes in. We are about to explore the concept of data augmentation, including what it is, its accepted techniques, and how you can use it to enhance your machine learning model.
But before we dive in, let’s review some basics.
What Is Machine Learning?
Machine learning is a subdivision of Artificial Intelligence. It emphasizes statistical techniques to develop intelligent computer systems to learn from available databases. Machine learning involves systems sifting through data, searching for patterns, and adjusting their actions accordingly, thereby "learning" from experience. For example, software applications employ machine learning to increase their accuracy in predicting outcomes without being programmed.
Machine learning is essential for voice recognition, image recognition, and the medical and financial sectors. In addition, companies like Facebook, Yelp, Google, Twitter, and Salesforce employ machine learning.
What Is Data Augmentation?
Sometimes, situations called “overfitting” pop up in machine learning. Overfitting is defined as a situation where the Machine Learning-based statistical model’s data fits precisely against its training data. Therefore, the algorithm can’t perform accurately against its new, unseen data, which is the whole point.
Machine learning experts turn to data augmentation to resolve the overfitting problem.
Data augmentation is a process used to boost the amount of new data even when there is no new data on hand! Data augmentation creates new and representative data by adding slightly altered copies of existing data or using newly created synthetic data from the existing data.
Incidentally, synthetic data is artificially created information, as opposed to data generated by actual real events. It can be created to meet the specific conditions or needs that otherwise don't exist in existing data. Thus, synthetic data is considered a form of data augmentation.
Data scientists use data augmentation to prevent the overfitting as mentioned above, expand an initial data set that's too small for training purposes, or even get a little extra performance from their deep learning model.
When we’re dealing with machine learning and deep learning, the larger the dataset, the better. Data augmentation helps the process by boosting the already existing data.
Now let's check out some accepted data augmentation techniques.
Data Augmentation Techniques
Data augmentation techniques involve making slight changes to the existing data. It’s like rephrasing a sentence. We can break down data augmentation into:
Image augmentations are by far the most popular augmentation technique. Perhaps it’s because there are many possible variations, and many of them are easy to pull off. These techniques include:
- Kernel Filters: This technique involves sharpening or blurring an image.
- Random Erasing: Delete a small part of the current image.
- Flipping: You can flip an image from a horizontal to a vertical configuration.
- Color Space Transformation: You can intensify any existing color or change the RGB color channels.
- Re-Scaling: This technique involves changing the image scale. You can scale inward or outward. If you scale inward, it will be smaller than the original image size. If you scale outward, the image will be larger than the original.
- Geometric Transformations: This method includes randomly flipping, rotating, cropping, or translating images, among other methods.
- Mixing Images: Although it may sound strange, you can combine images.
Audio augmentation isn’t as easy as image augmentation, but it provides an excellent opportunity to add some variety to your augmentation efforts. Ultimately, what matters is, you’re changing the data slightly.
- Speed: You can change the speed of the sound file or tape.
- More Sounds: You can inject extra noise into the audio file.
- Pitch: This method means that you shift the audio pitch.
This technique is as easy as image augmentation and maybe more straightforward!
- Sentence/Word Shuffling: With this technique, you change the sentences or words' order while still retaining the overall coherence.
- Word Replacement: This data augmentation technique involves replacing existing words with synonyms. So, for example, “This film is stupid” could become “This movie is idiotic.”
- Syntax-Tree Manipulation: You paraphrase an existing sentence to be correct grammatically while using the same words.
- Back Translation: This technique is very effective and rather fun. Take a sentence written in your language, run it through a translator to a different language, then re-translate it back again to the original language. For example, take the sentence “I don’t like how this smells.” If you translate it into Spanish, it comes out as “no me gusta como huele esto.” But if you translate it back to English, you get “I don't like the way this smells.” And there you have it: instant text augmentation!
- Random Deletion: Although this method results in awkward text, it works. So, the sentence “I will not buy this record, it is scratched” turns into “I will not buy this, it scratched.” The sentence makes less sense, but it’s still a viable augmentation.
When you look at all these augmentation techniques as an aggregate, you can see how simple it is to boost your machine learning data and improve the overall robustness of your algorithms without exerting too much effort.
Data Augmentation to Improve Machine Learning Models
By now, you have become aware of the importance of data augmentation in machine learning. Let's focus on some best practices, tips, and tricks for using data augmentation deep learning to improve the overall machine learning model.
- For starters, you must choose proper augmentations for your project. For example, let’s say that you’re trying to detect a face on an image. You select random erasing as the augmentation technique on the image file, but suddenly your model doesn’t work well, even on training. That's because the image has no face since the augmentation technique randomly erased it! So make sure to use logic and common sense when you choose your data augmentation technique.
- Don’t use too many augmentations in one sequence. You may wind up creating an entirely new observation that has little or nothing in common with the original training or testing data. In other words, please don't overdo it.
- Time libraries provide the data structures and functions needed to perform time calculations, retrieve the system time, and format output strings that display the time in several standard formats.
- Before you begin training with your augmented data, display the data such as text or images in the notebook, or listen to the converted audio sample. It's very easy to cause an error when you're forming an augmenting pipeline. That's why you should review your work and double-check the results.
What Sorts of Challenges Does Data Augmentation Pose?
No process comes without at least a few obstacles or requirements, and data augmentation is no exception. Here are the challenges to watch for:
- Beware of Bias: If a real dataset includes biases, the data augmented from the set will also have biases. Therefore, you must identify an optimal data augmentation strategy.
- We Need New Data: The data augmentation world must create new studies and research for building new and additional synthetic data.
- Quality Evaluation: As more organizations turn to data augmentation methods, there will be an increased need to evaluate their output quality. As a result, businesses and other institutions must create evaluation systems to address this need.
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