State-of-the-art machine learning models need quantitative and qualitative labeled datasets for better performance. However, It is time-consuming and expensive to annotate large amounts of data with millions of attributes per data point.
Synthetic data helps in addressing the problems associated with data collection, data annotation, and data quality assurance.
Here’s what we’ll cover:
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Synthetic data is a form of data that mimics the real-world patterns generated through machine learning algorithms. Many sources identify synthetic data for different purposes, and types of data include:
Synthetic data for computer vision can be RGB images, segmentation maps, depth images, stereo-pairs, LiDAR, or Infrared images. Synthetic data for images and videos are typically created using a generative model resembling the latent space of the real-world data. The generative models mainly used for synthetic data generation include:
Engineers often require highly quantitative accurate, and diverse datasets to train and build accurate ML models. Synthetic data helps in reducing the costs of data collection and data labeling. In addition to lowering costs, synthetic raw data helps address privacy issues associated with sensitive real-world data.
Furthermore, it reduces bias compared to real data as the developer controls the distribution of synthetic data. It can provide higher diversity by including anomalies that are difficult to source from authentic data.
Here are some of the key benefits:
Developers know about the technique of data augmentation.
The images can be rotated in an already painted way to produce a completely new image in another way. The removal and use of raw data in databases are now increasingly popular. Data anonymization is used for text, which is primarily used by industries such as finance and healthcare.
On the contrary, synthetic data is new data generated from a reference distribution of the real data.
Using the augmentation techniques on the original image, we can achieve transformations on the original image (rotation, flipping, etc.) whereas using a synthetic image generation technique, we can tweak the distribution to create a new data.
There are two ways to generate synthetic data for computer vision.
Essentially, GANs consist of two neural network agents/models (called generator and discriminator) that compete in a zero-sum game, where one agent's gain is another agent's loss.
The intuition behind the objective function of GANs is to generate data points that mimic data from the training set and fool the discriminator into distinguishing between real and generated samples.
Algorithm using GANs to generate synthetic data
Over the years, many architectural variations and improvements over the original GAN idea have been proposed in the literature. Today, most GANs are loosely based on the DCGAN (Deep Convolutional Generative Adversarial Networks) architecture, formalized by Alec Radford, Luke Metz, and Soumith Chintala in their 2015 paper.
In the VAE model, the encoder compresses the real dataset into a compact form and transmits it to the decoder. The decoder then generates an output which is a representation of the real dataset. The system is trained by optimizing the correlation between input and output data.
Algorithm to generate synthetic data using VAE
Here is the basic workflow for 3D Rendering-based SD generation:
Many companies avoid using GANs for generating synthetic data due to following reasons
On 07 Mar 2022, Google researchers Klaus Greff, Francois Belletti, and Lucas Beyer released their research paper on Kubric: A scalable dataset generator. It is an open-source Python framework that allows you to create photo-realistic synthetic data.
Now, let's have a look at some of the most popular applications for synthetic data in computer vision.
Self-driving autonomous technology can dramatically reduce collision rates resulting from distracted driving. Automakers and autonomous vehicle (AV) manufacturers use real world data to train, test, and validate roadway driver safety monitoring systems.
While a handful of companies may be able to afford the process of producing and testing millions of vehicles in various geographical environments, most OEMs do not have sufficient resources or vehicles with the capability to provide such datasets.
Synthetic data combines techniques from the movie and gaming industries (simulation, CGI) with generative deep neural networks (GANs, VAEs), allowing car manufacturers to engineer realistic datasets and simulated environments at scale without driving in the real world.
Sharing data safely is one of the biggest challenges in the healthcare industry today. Synthetic data, or data that is artificially manufactured rather than generated by real-world events, is a promising technology for helping healthcare organizations to share knowledge while protecting individual privacy.
Researchers at Gretel.ai and Illumina built state of an art framework to generate high-quality synthetic datasets for genomics using Artificial Intelligence. The synthetic datasets created based on real world data offers enhanced privacy guarantees that enable life science researchers, to quickly test ideas through open access to data without compromising patient privacy.
Caper - a startup making intelligent shopping carts that enable customers to shop without waiting in the checkout line. Caper used synthetic images of store items that captured different angles and trained the deep learning algorithm. The company states that its shopping carts have 99% recognition accuracy.
Nvidia created a robotics simulation application and synthetic data generation tool Isaac Sim to develop, test, and manage Artificial intelligence-based robots working in the real world, e.g., in manufacturing plants.
Improving performance for challenging AI-based computer vision applications requires large and diverse datasets that replicate the inherent distribution of the target domain. There are many other scenarios where you can apply this process and use synthetic data to increase the robot’s understanding of its environment and how it should behave.
Researchers believe that synthetic data is essential for the further development of deep learning and will play an increasingly important role in the future. According to a Gartner report, by 2030, synthetic data will completely overtake real data in the AI model development process.
However, the report also highlights there are challenges to synthetic data adoption:
Here are a couple of widely used high-quality synthetic datasets.
SVIRO is a Synthetic dataset for Vehicle Interior Rear seat Occupancy detection and classification. The dataset consists of 25.000 sceneries across ten different vehicles and we provide several simulated sensor inputs and ground truth data.
This dataset contains 144k stereo image pairs generated from 18 camera viewpoints of three photorealistic virtual environments with up to 10 objects (chosen randomly from the 21 object models of the YCB dataset) and flying distractors.
Want to generate synthetic data to train your computer vision models?
Have a look at this cherry-picked list of the best synthetic data generation tools:
The Chooch AI platform can automatically generate synthetic images and corresponding bounding box annotations using the OBJ 3D geometry files and the associated.MTL texture file in a matter of seconds.
The Datagen solution is a fully customizable sandbox for exposing systems to dynamic environments of 3D spaces, people, and objects.
Parallel Domain's synthetic data platform provides utilities to generate high-quality data. They specialize in synthetic data generation for ADAS systems.
A vendor of a synthetic data generation platform for Computer Vision. They specialize in retail SKU data.
A vendor of a synthetic data generation platform for computer vision.
Here’s a recap of everything we’ve covered:
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