The process of artificially increasing the size of a dataset by generating additional examples using techniques such as flipping, rotating, cropping, or adding noise to the original data.
A privacy-enhancing technique that adds random noise to a dataset to protect the privacy of individuals while still allowing for useful statistical analysis.
A class of machine learning models that use a two-part architecture to generate synthetic data that is similar to real-world data. GANs consist of a generator network that produces synthetic data, and a discriminator network that tries to distinguish between the synthetic data and real data. The generator network is trained to produce data that is increasingly difficult for the discriminator to distinguish from real data. GANs have been used to generate synthetic images, videos, and audio, and have applications in areas such as image synthesis, data augmentation, and unsupervised learning. However, GANs can be difficult to train and can suffer from instability and mode collapse, where the generator produces only a few distinct outputs, rather than a diverse set of outputs.
An approach to developing and deploying AI systems that takes into account ethical, social, and legal considerations, as well as the potential impact of the technology on individuals and society. Responsible AI aims to ensure that AI systems are transparent, fair, and accountable and that they do not reinforce biases or perpetuate discrimination. It involves considering the ethical implications of the data used to train and test AI models, the potential unintended consequences of AI systems, and the social and legal frameworks in which they operate.