Abstract: A case study of efficiently solving a real-world computer vision problem using a combination of labelled real-world data and synthetic data, combining the strengths of each data type. It considers best practices for combining the datasets and showcases the benefits of a platform approach using Appen's platform for real world sourcing and labelling and the Mindtech Chameleon platform to generate the synthetic data.
Bio: Romain is one of Appen’s Senior Manager’s, overseeing and supporting their European client-base with Appen’s breadth of Data for the AI Lifecycle services (data sourcing, annotation/labelling, and model evaluation). Romain came from the localization industry and noted firsthand the advancements and impacts of ML/AI via Machine Translation/Transcription, ASR and TTS offerings. Therefore, he saw a transition into the world of AI/ML as the next logical step. Passionate about ethically sourced, high-quality labelled data, which powers Machine Learning/AI programmes for good.