Search Results for author:
Found 3 papers, 0 papers with code
Augmenting Knowledge Distillation With Peer-To-Peer Mutual Learning For Model Compression
Knowledge distillation (KD) is an effective model compression technique where a compact student network is taught to mimic the behavior of a complex and highly trained teacher network.
Leveraging Different Learning Styles for Improved Knowledge Distillation in Biomedical Imaging
These experimental results demonstrate that knowledge diversification in a combined KD and ML framework outperforms conventional KD or ML techniques (with similar network configuration) that only use predictions with an average improvement of 2%.
Understanding Calibration of Deep Neural Networks for Medical Image Classification
We considered fully supervised training, which is the prevailing approach in the community, as well as rotation-based self-supervised method with and without transfer learning, across various datasets and architecture sizes.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
