Data Augmentation: A Tactic to Improve the Performance of ML
Models of machine learning can do amazing things when they are equipped with enough training data. For many applications, however, access to reliable data is a challenge. One solution is data augmenting, which creates new training models by combining existing examples. Data Augmentation is an inexpensive and effective way to increase the efficiency as well as the accuracy of model-based learning in an environment with limited data.
If model-based machine learning is taught on only a few samples, they can overfit. Overfitting occurs whenever an ML model is able to perform accurately in its examples of training but fails to apply the model to data that is not seen. There are a variety of methods to prevent overfitting in machine learning, such as using different algorithms, altering the model's architecture, or altering hyperparameters. However, the most effective solution to overfitting is to add higher-good quality data to the data. But collecting additional training instances can be expensive or time-consuming. Sometimes, it's impossible. This becomes more challenging when using supervised learning programs in which training examples have to be labelled by experts in the field.
One method to enhance the diversity of the training data is to create duplicates of the already in use and then make minor changes to the original data. This is referred to as data augmentation. For instance, let's say we have twenty photos of ducks in our classification dataset. By making duplicates of our duck photos by flipping them vertically, we have doubled the number of exercises to be used in the "duck" class. It is possible to use other transforms such as cropping, zooming, translation, and rotation. Combining the transforms to increase our arsenal of unique training examples is also possible.
Data augmentation does not have to be restricted to the manipulation of geometric shapes. Adding noise, altering the colour settings, or adding other effects like blur and sharpening filters may assist in repurposing old training models as fresh data. Data augmentation is especially beneficial for supervised learning as it already has the label and doesn't have to spend extra time to note the new examples. Data augmentation is also beneficial for different classes that employ machine-learning algorithms, such as unsupervised, contrastive, and models that are generative.
Data augmentation has now become an accepted method of developing machine learning models in computing vision. The most popular deep and machine learning programming libraries provide an easy-to-use feature to integrate data augmenting in the ML training pipeline. Data augmentation isn't only limited to images but can be applied to different kinds of data. In the case of text data, verbs and nouns may be substituted with equivalents. In audio files, training examples can be altered by adding noise or altering the speed of playback.
Data augmentation isn't a panacea to solve all of our data-related problems. It can be thought of as a boost in performance for our models using ML. Based on the application we are targeting; it is still necessary to have an extensive training dataset that includes enough instances. In certain cases, the training data could be too small for data augmentation in order to benefit. In these instances, it is necessary to gather more data until we have reached an acceptable threshold before using data enhancement. Sometimes, we may be able to use transfer learning, which is where we develop an ML model on the general data set and reuse it by fine-tuning its upper layers based using the data available to target your application.
Data augmentation doesn't also solve other issues, such as biases in the training data. Data augmentation requires adjustments to deal with other issues like imbalanced classes.