Ai+ Training

Why “Small Data” is important for advancing AI? Will Big Data models work correctly, if trained on synthetic data? Can we properly define and train a Neural Network from a limited dataset, by augmenting its data with extra knowledge (meta-data)?
This talk is about the role of small data in the future of AI. Efforts have already begun in this direction. Although the current mantra of deep learning says ‘you need big data for AI’, more often than not, AI becomes even more intelligent and powerful if it has the capability to be trained with small data. Some AI solutions that rely only on small data outperform those working with big data. Some other AI solutions use big data to learn how to take advantage of small data.
The presenter will illustrate practical and visual examples from the Fashion Retail sector, some approaches and methods for using Small and Synthetic Data for the training of the AI and Big Data systems, starting from the “big difference” but common nature of Small Data and Big Data. It will be explained in detail the case of a Visual Search service, whose training is initially based on 3D CAD models and their metadata, combined with real pictures in the later stages of training.