Abstract: In recent years, fuelled by the advances in supervised machine learning, we have seen astonishing leaps in the application of deep neural networks. Despite the remarkable results, these models are data-hungry and their performance relies heavily on the quality and size of the training data. In real-world scenarios, this can increase the time to value add significantly for businesses as collecting huge amounts of labelled data is usually very time and cost consuming. This phenomenon—known as the cold start problem—is a pain point for almost any AI company that wants to scale. In this talk, we demonstrate how this problem can be addressed by aggregating data across sources and leveraging previously trained models with using domain adaptation and ensemble learning techniques.
Bio: Franziska Kirschner is the Research and Product Lead of Car Inspection at Tractable. Her team uses machine learning to automate car damage appraisal across a range of applications. Her research interests include domain adaptation, and multitask- and multi-instance learning. In a previous life, she did a PhD in Physics at the University of Oxford. In her spare time, she enjoys cooking and making bad puns.
Franziska Kirschner, PhD
Research and Product Lead | Tractable