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Doubly Perturbed Task Free Continual Learning
Specifically, for input perturbation, we propose an approximate perturbation method that injects noise into the input data as well as the feature vector and then interpolates the two perturbed samples.
Online Continual Learning on Hierarchical Label Expansion
To address this challenge, we propose a novel multi-level hierarchical class incremental task configuration with an online learning constraint, called hierarchical label expansion (HLE).
All-in-One Image Restoration for Unknown Degradations Using Adaptive Discriminative Filters for Specific Degradations
Image restorations for single degradations have been widely studied, demonstrating excellent performance for each degradation, but can not reflect unpredictable realistic environments with unknown multiple degradations, which may change over time.
Empirically Accelerating Scaled Gradient Projection Using Deep Neural Network For Inverse Problems In Image Processing
Recently, deep neural networks (DNNs) have shown advantages in accelerating optimization algorithms.
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