Fine-tuning with Very Large Dropout: Leveraging Rich Representations

The author explores the use of very high dropout rates in fine-tuning pre-trained models to achieve rich representations, surpassing ensemble methods and weight averaging. This approach leverages existing features rather than creating new ones, leading to improved out-of-distribution performance.