Synthesizing real-world distributions from high-dimensional Gaussian Noise with Fully Connected Neural Network

ArXi:2604.09091v1 Announce Type: new The use of synthetic data in machine learning applications and research offers many benefits, including performance improvements through data augmentation, privacy preservation of original samples, and reliable method assessment with fully synthetic data. This work proposes a time-efficient synthetic data generation method based on a fully connected neural network and a randomized loss function that transforms a random Gaussian distribution to approximate a target real-world dataset.