Most ML models’ success is determined by the quantity and variety of data. Data augmentation is used by businesses to minimize their reliance on training data preparation and to construct more accurate machine learning models faster.
- Data augmentation is a method for creating data for ML models.
The Significance of Data Augmentation
Machine learning applications, particularly in the deep learning area, continue to diversify and grow at a rapid pace. Data augmentation approaches might be a useful tool in combating the problems that the artificial intelligence field is facing.
- Data augmentation is beneficial for improving the performance and results of machine learning models by generating fresh and diverse instances for training datasets.
When a machine learning model’s dataset is rich and sufficient, the model performs better and is more accurate.
Data collection and labeling may be time-consuming and expensive operations for machine learning models. Companies can minimize operating expenses by transforming datasets using data augmentation techniques.
Cleaning data is one of the processes in creating a data model, and it is required for high accuracy models. However, if cleaning lowers the representability of the data, the model will be unable to give accurate predictions for real-world inputs. Data augmentation approaches improve the robustness of machine learning models by introducing variables that the model may encounter in the actual world.
Classic and advanced techniques in Data Augmentation
For training data, computer vision applications employ standard data augmentation approaches. There are both basic and complex data augmentation approaches for picture recognition and natural language processing.
Making basic changes to visual data is common for data augmentation. Generative adversarial networks (GAns) are also used to generate fresh synthetic data.
Padding, random rotating re-scaling, and vertical and horizontal flipping translation are examples of traditional image processing operations for data augmentation. Additionally, cropping, zooming, color adjustment, darkening, and brightness are all classic techniques applied in image classification.
On the other hand, advanced data augmentation models are being developed:
- Adversarial training/machine learning: It produces adversarial instances that disturb a machine learning model and injects them into a dataset to train.
- Generative Adversarial Networks: GAN algorithms can learn patterns from input datasets and generate new instances that are similar to training data.
- Neural style transfer models may combine content and style images as well as separate style from content.
- Reinforcement learning models teach software agents how to achieve their goals and make judgments in a virtual environment.
Pros and Cons of Data Augmentation
The following are some of the advantages of data augmentation:
- Increasing the accuracy of model prediction
- Incorporating more training data into the models
- Avoiding data scarcity in order to improve models
- Minimizing data overfitting (a statistical mistake that occurs when a function fits too closely to a restricted number of data points) and increasing data variability
- Improving the models’ generalization capacity, assisting in the resolution of class imbalance concerns in categorization
- Lowering the cost of data collection and labeling
Here are some disadvantages of data augmentation:
- Companies must develop quality evaluation mechanisms for enhanced datasets. As the usage of data augmentation methods grows, it will be necessary to examine the quality of their output.
- To produce fresh/synthetic data with sophisticated applications, the data augmentation area requires new research and study. For example, utilizing GANs to generate high-resolution pictures is difficult.
- If the original dataset has biases, the data-enhanced from it will also have biases. As a result, determining the best data augmentation technique is critical.
