NeurIPS’22 Cross-Domain MetaDL Challenge: Results and lessons learned

Deep neural networks have demonstrated the ability to outperform humans in multiple tasks, but they often require substantial amounts of data and computational resources. These resources may be limited in certain fields. Meta-learning seeks to overcome these challenges by utilizing past task experiences to efficiently solve new tasks, achieving better performance with limited training data and modest computational resources. To further advance the ChaLearn MetaDL competition series, we organized the Cross-Domain MetaDL Challenge for NeurIPS’22. This challenge aimed to solve “any-way” and “any-shot” tasks from 10 domains through cross-domain meta-learning. In this paper, authored collaboratively by the competition organizers, top-ranked participants, and external collaborators, we describe the technical aspects of the competition, baseline methods, and top-ranked approaches that have been open-sourced. Additionally, we provide a detailed analysis of the competition results. Lessons learned from this competition include the critical role of pre-trained backbones, the necessity of preventing overfitting, and the significance of using data augmentation or domain adaptation techniques in conjunction with extra optimizations to improve performance.