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We consider the problem of class incremental learning (CIL), where an agent aims to learn new classes continually without forgetting previous ones.
TCN avoids catastrophic forgetting by fixing all learnt parameters and leverages prior knowledge contained in networks. Experiments on three widely used ...
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Apr 2, 2022 · We present a novel class incremental learning approach based on deep neural networks, which continually learns new tasks with limited memory for storing ...
Mar 18, 2024 · To alleviate the stability-plasticity dilemma in our incremental task, we propose a novel Relationship-Guided Knowledge Transfer (RGKT) method ...
A main component for incrementally learning new classes for object detection, is the selection of proposals that will be used for transferring knowledge from ...
Jun 19, 2023 · Few-shot class-incremental learning (FSCIL) aims to continually learn new classes using a few samples while not forgetting the old classes.
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Federated Class-Incremental Learning (FCIL) focuses on continually transferring the previous knowledge to learn new classes in dynamic Federated Learning ...
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Standard deep learning based object detectors suffer from catastrophic forgetting, which results in performance degradation on old classes as new classes ...
We propose a class-incremental learning paradigm with adaptive knowledge transfer. This paradigm leverages crucial self-learning factors to transfer the ...
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Usually, these approaches can be broadly categorized into three directions, i.e., regularization-based [3, 4, 5], rehearsal-based [6, 7, 8], and architecture- ...