Master Data Augmentation in 5 minutes with no prior expertise

Precision of DL models depends on the quality and quantity of data training.

Data insufficiency is the main challenge when creating DL models. Collecting relevant data for production can be time-consuming and costly.

Businesses can use active data augmentation to reduce data scarcity and costly methods of data collection. This helps to reduce the need for training samples, and allows for rapid development of highly accurate artificial intelligence models.

What’s data augmentation?

Data augmentation artificially increases data quantity by creating new data points from pre-existing data. This process involves minor changes to the data, or the use ML models to create data points for unique data.

It is possible to ask about the differences between synthetic and augmented data.

Synthetic Data

Synthetic data is data that is artificially generated from images. Multiplicative adversarial networks are the most common way to generate synthetic data.


This data is a resultant of new images with slight geometrical modifications, such as translation, flipping or noise addition. It is used to increase variety in training sets.

It is important to recognize the importance of confidentiality concerns when it comes to data collection and usage.

Organizations and researchers prefer synthetic data building techniques to create datasets. The limitations of augmented data, such as the lack of similarity to original data makes it more popular than synthetic.

Data Augmentation is essential.

Here are some details that have made data amplification techniques so popular over the past few years.

Performance Boost For ML Models

Data augmentation techniques are used in nearly every pioneering DL application. This includes image recognition, object detection and semantic segmentation.

Augmented data improvement helps in boosting the outputs and efficiency of DL model by creating new and diverse instances to train datasets.

Lower Operational Costs

Data collection and labeling are time-consuming and costly for DL models. Companies can reduce expenses by modifying existing data using data augmentation methods.

Data Augmentation Limitations

Data Augmentation comes with its own set of limitations and challenges.

  1. Quality assurance costs for augmented datasets.
  2. R&D to build synthetic data and pioneering applications
  3. It is difficult to authenticate image amplification methods.
  4. It is important to find a viable augmentation method for data.
  5. In newer data sets, the inborn predisposition to real world data is maintained

Let’s now examine the feasibility and working mechanisms of data augmentation.

Data Augmentation Cases

Data augmentation is the most common method to artificially increase the amount data – the data required to train strong AI models.

This is especially important in niches or areas that make it difficult to acquire quality data. Here is a list highlighting industries that use data augmentation to generate data.


Curating datasets for medical imaging applications is not an option. Obtaining large numbers of annotated samples, from experts, is costly and time-consuming.

The network that has been trained to augmentation must be more precise and robust than the expected variations of the same Xray imaging.

Self-Driving Vehicles

Self-driving cars is another important application of data augmentation.

reinforcement learning is used to create simulation environments that can be used for training and testing AI systems in cases of data scarcity.

Data augmentation has many applications. The simulation environments can be modelled to generate real-world scenarios.

Data augmentation, however resourceful, is not without its challenges.

What are the key challenges it faces?

To ensure and verify the quality of data, organizations must create valuation structures. Assessments of the quality of datasets will become more important as data augmentation becomes more common.

Data augmentation requires new research and studies to generate new/synthetic data for cutting-edge applications. It can be difficult to generate high-resolution imagery using GANs.

If the datasets are predisposed, then the data that is augmented with them will also have biases. It is therefore crucial to identify the best data augmentation strategies.

To sum up;

  1. Data augmentation is actually the artificially growing of data through the generation of newer points from existing data.
  2. Advanced data augmentation models include adversarial MML, GANs and neural style transfer.
  3. Data augmentation can be used when data collection is more difficult.
  4. Two of the most prominent industries that use data augmentation are autonomous cars and health.

For more information on Data Augmentation, visit Qwak.

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