Search Results for author:
Found 4 papers, 1 papers with code
Domain-Aware Fine-Tuning: Enhancing Neural Network Adaptability
By leveraging batch normalization layers and integrating linear probing and fine-tuning, our DAFT significantly mitigates feature distortion and achieves improved model performance on both in-distribution and out-of-distribution datasets.
Learning to Learn Unlearned Feature for Brain Tumor Segmentation
We propose a fine-tuning algorithm for brain tumor segmentation that needs only a few data samples and helps networks not to forget the original tasks.
Beyond Examples: Constructing Explanation Space for Explaining Prototypes
As deep learning has been successfully deployed in diverse applications, there is ever increasing need for explaining its decision.
Variational saliency maps for explaining model's behavior
Saliency maps have been widely used to explain the behavior of an image classifier.
