The rapid advancements in Artificial Intelligence (AI) have been driven by new technologies utilizing real-world data. While real-world data trains AI models, its availability is limited. As AI continues to progress, researchers and experts acknowledge the need to explore new sources of data collection. Noema employs a series of innovative techniques that have changed the way we train neural networks by moving beyond real-world data. There are several cutting-edge approaches that enable us to explore new possibilities in data collection and AI training. 

Stable Diffusion

Stable Diffusion expands data horizons by capturing and extrapolating patterns from the existing dataset to generate more data or underrepresented real-world cases. Additionally, it enhances the reliability of models by generating diverse and comprehensive test cases, especially where real-world data is limited.

Data Augmentation and Dataset Expansion

While Stable Diffusion is a state-of-the-art breakthrough in data synthetization and dataset expansion, there are also sets of techniques to extend the data set that have been used since the earliest days of AI, known as data augmentation. It consists of applying classical image processing operations like blur, colorspace shifts, and random noise, in order to obtain higher diversity in the training datasets. Time and time again it is proven that by applying data augmentation techniques, we can increase the quantity and diversity of training examples, enabling AI models to learn the desired features more effectively and efficiently.

Synthetic Data

Using real data can significantly increase the final cost of a solution because it is not always readily available and the process of collecting and labeling data is time-consuming. Synthetic data is a revolutionary approach that pushes the boundaries of neural network training. By creating artificial samples that are very similar to real samples, datasets can be extended nearly infinitely, while your labeling costs become virtually non-existent.

Two approaches for collecting synthetic data exist: Photorealistic image generation from 3D engines and generative AI models, namely Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Both techniques are powerful tools for generative modeling, but they employ different approaches and have distinct characteristics, making them suitable for various applications in synthetic dataset generation and data augmentation.

Active Learning

Labeling large amounts of data is often a laborious process. Active learning methods aim to optimize this process by selecting the most informative samples for annotation. This iterative technique allows the model to actively query the unlabeled data and prioritize samples that are most likely to improve its performance. Active learning accelerates the data labeling process and improves the efficiency of AI model training. Furthermore, active learning techniques have the potential to adapt and learn from the labeled data, continuously refining the model's performance over time. This dynamic and adaptive nature of active learning makes it a valuable tool for data scientists and researchers, maximizing their limited labeling resources and helping them achieve higher accuracy and performance in AI applications.

Weakly Supervised and Self-Supervised Learning

Synthetic data generation can be combined with weakly supervised or self-supervised learning approaches to reduce the reliance on expensive and precise annotations. These methods use proxy labels, auxiliary tasks, or self-supervision to train models with less labeled data. By using synthetic data in conjunction with these learning methods, models can learn meaningful representations and achieve competitive performance with limited labeled data.

Beyond Real-World Data at Noema

At Noema, we developed an efficient, automated data generation, collection, and labeling tool by integrating various cutting-edge and traditional approaches. This innovative tool enables us to handle challenging tasks rapidly and effectively. 

As an example, building our Flood Detection application took several months between data prospection and manual/semi-automatic labeling, in order to build a 7000+ image training dataset. If we were to start this process today, it would be completed in a matter of weeks. Of course, in the meantime, we have already taken advantage of these new techniques to more than double the dataset size.

The impact of these innovative techniques cannot be overstated. By moving beyond the limitations of real-world data and harnessing the power of different techniques, we unlock new possibilities in AI-based applications. Using new approaches not only accelerates the development process but also provides cost-effective solutions, resulting in improved accuracy and better solutions overall. Cheaper, more accurate, and state-of-the-art applications can be developed by leveraging the benefits of these techniques. Moreover, the efficiency and speed of our comprehensive toolset enable us to develop cutting-edge applications that are not only more cost-effective but also more accurate and state-of-the-art. 

To learn more about Noema and our applications, visit www.noema.tech.