// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Data Augmentation
Techniques used to artificially increase the amount of training data by creating modified versions of existing data, often by applying transformations.
TECHNICAL DEFINITION
Data augmentation is a set of techniques used to increase the diversity of training data by creating new, slightly modified copies of existing data, such as rotation, flipping, or cropping for images, to improve model generalization and reduce overfitting.
BACKGROUND
Retrieval-augmented generation (RAG) is a technique that enables large language models (LLMs) to retrieve and incorporate new information from external data sources. With RAG, LLMs first refer to a specified set of documents, then respond to user queries. These documents supplement information from the LLM's pre-existing training data. This allows LLMs to use domain-specific and/or updated information that is not available in the training data. For example, this enables LLM-based chatbots to access internal company data or generate responses based on authoritative sources.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Data expansion
- synthetic data generation
- artificial data
USAGE NOTE
It's particularly effective in computer vision to make models more robust to variations in input.
DEVELOPERS
Organizations developing technology related to Data Augmentation.
Engages in extensive research and development of advanced machine learning techniques, including pioneering work in data augmentation strategies (e.g., AutoAugment, RandAugment) across various modalities to improve model generalization and robustness, critical for AI engineering and prompt design.
Conducts cutting-edge AI research, contributing significantly to data augmentation methodologies for improving model performance, efficiency, and robustness, essential for training large-scale AI models and developing robust AI systems.
Investigates fundamental and applied AI research, with projects frequently involving sophisticated data augmentation strategies to enhance the performance, reliability, and data efficiency of machine learning models across diverse applications.
Offers a comprehensive suite of AI/ML services and tools (e.g., Amazon SageMaker) that support and often integrate data augmentation techniques, enabling developers to build, train, and deploy high-performing AI models more efficiently, even with limited datasets.
Develops AI technologies and solutions for enterprise, including research into various data augmentation techniques to address data scarcity, improve model accuracy, and enhance the robustness of AI systems in complex, specialized domains.
A leader in GPU-accelerated computing and AI, NVIDIA develops extensive software stacks and research, including tools and frameworks (e.g., DALI library) that facilitate high-performance data loading and augmentation critical for deep learning training across various AI applications.
Provides open-source libraries and platforms for natural language processing and machine learning. While not a dedicated data augmentation company, their ecosystem (e.g., 'datasets' library) facilitates and benefits from data augmentation techniques for training and fine-tuning robust language models and other AI systems.
Specializes in programmatic labeling and data creation platforms. Their approach helps AI engineers build high-quality training datasets faster using techniques like weak supervision and synthetic data generation, effectively augmenting available data for more robust model development.