Search Results for author:
Found 4 papers, 0 papers with code
BI-MAML: Balanced Incremental Approach for Meta Learning
The balanced learning strategy enables BI-MAML to both outperform other state-of-the-art models in terms of classification accuracy for existing tasks and also accomplish efficient adaption to similar new tasks with less required shots.
Knowledge Distillation Circumvents Nonlinearity for Optical Convolutional Neural Networks
However, a major challenge in using this spectral approach, as well as in an optical implementation of CNNs, is the inclusion of a nonlinearity between each convolutional layer, without which CNN performance drops dramatically.
Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation
Many medical datasets have recently been created for medical image segmentation tasks, and it is natural to question whether we can use them to sequentially train a single model that (1) performs better on all these datasets, and (2) generalizes well and transfers better to the unknown target site domain.
TKIL: Tangent Kernel Approach for Class Balanced Incremental Learning
In our work, we propose to address these challenges with the introduction of a novel methodology of Tangent Kernel for Incremental Learning (TKIL) that achieves class-balanced performance.
