Improving Generalisation of Meta-learning for Sequential Data Anomaly Detection


Abstract Meta-learning, or learning to learn, has proven to be a popular framework in dealing with few shot problems in recent times. Most of the work using this family of techniques focuses on the image domain, leaving many other interesting data modalities and settings largely unexplored. This project looks at expanding this scope of the available research by considering both time series data and the task of anomaly detection. In the first year of the PhD, work has largely been devoted to time series, focusing specifically on few-shot acoustic classification and its standardisation within literature.