Large Pretraining Datasets Don't Guarantee Robustness after Fine-Tuning in Image Classification
It is found that though continual learning methods help, fine-tuning reduces robustness across pretrained models, suggesting that starting with the strongest foundation model is not necessarily the best approach for performance on specialist tasks.
Jaedong Hwang, Brian Cheung, Zhang-Wei Hong et al.
· 0 citations