Data augmentation what is it?
How does data augmentation work?
The noticeable use cases?
Study data augmentation in its entirety for its techniques, plus you can begin training the AI models today.
The precision of DL models rests on the quantity, quality, and circumstantial significance of training the data.
But, data insufficiency is the chief challenge in making DL models. The use cases in production, collection of relevant data can become expensive and time taking.
To curb data scarcity and expensive methods for data collection – businesses leverage budget friendly and active data augmentation. This reduces reliance on gathering and groundwork of the training samples and helps build highly precise artificial intelligence models swiftly.
What is data augmentation?
Data augmentation artificially boost data quantity by producing newer data points via pre-existing data. The process includes the adding minor modifications to the data or the use of ML models for creating data points for dormant space in unique data –this amplifies the dataset.
You may ask how synthetic data and augmented data differ from each other.
This data is artificially generated without actual images, it is known as synthetic data. Synthetic data is mostly generated by multiplicative adversarial networks.
This type of data is resultant of novel images with slight geometrical alterations, which include translation, flipping, or noise addition – this is done to surge variety in training sets.
One must acknowledge the surge and stake of confidentiality concerns in regard to data usage and collection.
This is why organizations and researchers now prefer to use synthetic data building techniques for creating datasets. Owing to the limitations, including absence of similarities to original data, the augmented data remains mostly preferred in place of synthetic.
Why is Data Augmentation essential?
Below are some details owing to which the data amplification techniques are triumphant for few years now.
Performance Boost for ML Models
Data augmentation techniques have applications in almost every pioneering DL application, including image recognition, object detection, semantic segmentation, image classification , etc.
Augmented data improvements help in boosting working efficiency and outputs of DL models by creating diverse and new instances for the training of datasets.
Reduction in Operational costs
Data labeling and collection are expensive and time taking for DL models. Organizations lower expenses through modifying existing datasets with data augmentation methods.
Data Augmentation Limitations
Data Augmentation has its set of challenges and limitations, which include;
- Costs for quality assurance for augmented datasets.
- R&D for building synthetic data along with pioneering applications
- Authentication for image amplification methods is challenging
- Fetching a workable augmentation approach for data is significant
- The inborn predisposition of real world data continues in newer amplified data sets
Now let’s see the feasibility of data augmentation and its working mechanism.
Use Cases for Data Augmentation
Data augmentation is now the most popular method to increase the amount of data artificially – data that is needed to train robust AI models.
It is important for niches and areas where quality data acquisition becomes challenging. Below is a list of industries that commonly leverage data augmentation for generating data.
For medical imaging applications, curating datasets is not a workable option as acquiring a huge number of annotated samples from the experts is expensive and, not to mention, very time-consuming.
The network trained with augmentation has to be more accurate and robust than expected variations of the same X-Ray imaging.
Another important use case of data augmentation is self-driving vehicles.
Simulation environments are built using reinforcement learning mechanisms that help in testing and training AI systems when data scarcity becomes an issue.
The applications of data augmentation are endless as the simulation environments can be modeled per requirement for generating real-world scenarios.
As resourceful as data augmentation is – it does have some challenges.
What are its key challenges?
Organizations have to create valuation structures for guaranteeing and checking the quality of datasets. As the need and usage of data augmentation increases, assessments of their quality will be highly prioritized.
Also, data augmentation needs to create new research and studies for generating new/synthetic data with cutting-edge applications. For instance, the generation of high-resolution imaging using GANs can become challenging.
Considering if the real datasets have predispositions, the data augmented from these will have biases too. Hence the identification of optimal data augmentation schemes are critical.
To sum up;
- Data augmentation actually is the process of artificially increasing data by the generation of newer data points from pre-existing datasets.
- Advanced data augmentation models include adversarial ML, GANs, and neural style transfer.
- Data augmentation is used for situations where data collection in larger amounts is difficult.
- Autonomous cars and healthcare are the two prominent industries using data augmentation.
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