#Synthetic Data

Utilize a powerful open-source Python library designed for efficiency in synthetic data generation and model training. Create complex prompting workflows, seamlessly generate synthetic datasets, and train models using advanced techniques like quantization and LoRA. Ideal for researchers and developers, this tool facilitates reproducibility and straightforward sharing of datasets and models, enhancing machine learning projects with streamlined and optimized processes.

The survey paper explores the developing field of AI-generated images used as data sources, emphasizing the methodologies and various uses of synthetic visual data. It categorizes the content comprehensively, focusing on generative models and neural rendering, applied across 2D and 3D visual perception and medical data synthesis. By reviewing diverse methods such as generative adversarial networks and diffusion models, the paper examines new applications in image classification, segmentation, and self-supervised learning, providing insights into the future potential of AI-generated content across different industries.

The 'mtt-distillation' project employs a cutting-edge technique in 'Dataset Distillation,' optimizing synthetic images to emulate the training behaviours of genuine datasets. This ensures similar performance during tests. Leveraging expert networks, the project converts synthetic data for tasks such as ImageNet subset synthesis and texture creation, broadening AI model capabilities while conserving resources. The project's scalable solutions are suitable for areas like fashion and targeted image sets due to its tileable textures. Highlighted features include the generation of class-based textures, solid training model frameworks, and integration possibilities with various datasets, enhancing the effectiveness of synthetic dataset usage.

Gretel Synthetics offers robust tools for generating synthetic data with neural networks, supporting simple and dataframe modes. Utilize TensorFlow and PyTorch for enhanced timeseries handling and memory optimization with ACTGAN. Ensure data privacy with differential privacy, all within an easily integrable Python framework, backed by comprehensive documentation.

The REaLTabFormer framework utilizes sequence-to-sequence models and GPT-2 to generate realistic relational and tabular data. It's designed for dataset synthesis, handling both relational structures and independent observations effectively. Available on PyPi, REaLTabFormer is easy to install and use, demonstrating excellent performance in prediction tasks. It employs techniques like target masking and statistical bootstrapping to reduce overfitting, with a user-friendly interface for creating validators to ensure high-quality synthetic samples.

Learn about the transition from ydata-synthetic to ydata-sdk, offering enhanced privacy and performance in synthetic data generation. Explore how this open-source package simplifies the process using a single API for model optimization, leveraging GANs for dataset improvement, and ensuring privacy compliance and high-quality data outputs.