Abstract: A neural ODE-based conditional tabular generative adversarial network apparatus includes: a tabular data preprocessing unit for preprocessing tabular data composed of a discrete column and a continuous column; a Neural Ordinary Differential Equation (NODE)-based generation unit for generating a fake sample by reading a condition vector and a noisy vector generated based on the preprocessed tabular data; and a NODE-based discrimination unit for receiving a sample composed of a real sample or the fake sample of the preprocessed tabular data and performing continuous trajectory-based classification.

Abstract: An apparatus for data augmentation includes a mode separating unit generating minor class fake data from a latent vector, an embedding vector generating unit generating embedding vectors for major class original data, minor class original data, and the minor class fake data through a metric network, an auxiliary classifying unit classifying a class of the embedding vectors from the embedding vector generating unit, and feedbacking the classified result in the mode separating unit, and a classifying unit determining whether the input data is authentic by receiving the embedding vector of the minor class original data and the embedding vector of the minor class fake data from the embedding vector generating unit, and feedbacking the determined result in the mode separating unit.