Google Introduces 'Meta-Dataset' Benchmark for Few-Shot Learning

Deep learning's recent impressive breakthroughs have largely been based on huge quantities of manually annotated training data. Because appropriate large labelled datasets are often simply not available and manual labelling takes a lot of time, deep learning researchers are always on the lookout for ways to achieve the desired results with smaller amounts of data. Moreover, solving this data challenge can enable AI practitioners with limited resources to perform faster and cheaper model customization. Few-shot classification is a crucial component in the struggle to teach models using limited information. Although this is a hot research field, previous benchmarks could not reliably evaluate different models, which has hindered research progress. In a paper published at ICLR 2020 this month, Google AI researchers introduce Meta-Dataset, a large-scale and diverse benchmark for measuring the ability of few-shot classification models.