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Make Continual Learning Stronger via C-Flat
A general framework of C-Flat applied to all CL categories and a thorough comparison with loss minima optimizer and flat minima based CL approaches is presented in this paper, showing that our method can boost CL performance in almost all cases.
Progressive Learning without Forgetting
Learning from changing tasks and sequential experience without forgetting the obtained knowledge is a challenging problem for artificial neural networks.
A Bhattacharyya Coefficient-Based Framework for Noise Model-Aware Random Walker Image Segmentation
One well established method of interactive image segmentation is the random walker algorithm.
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