no code implementations • 17 Jun 2022 • Jinlin Xiang, Eli Shlizerman
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.
no code implementations • 3 Jun 2022 • Chenyu You, Jinlin Xiang, Kun Su, Xiaoran Zhang, Siyuan Dong, John Onofrey, Lawrence Staib, James S. Duncan
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.
no code implementations • 26 Feb 2021 • Jinlin Xiang, Shane Colburn, Arka Majumdar, Eli Shlizerman
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.
no code implementations • 12 Jun 2020 • Yang Zheng, Jinlin Xiang, Kun Su, Eli Shlizerman
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.