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Incremental class learning using variational autoencoders with similarity learning
Catastrophic forgetting in neural networks during incremental learning remains a challenging problem. Previous research investigated catastrophic...
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Incremental and sequence learning algorithms for weighted regularized extreme learning machines
The adoption of weighted regularized extreme learning machines (WR-ELMs) has been recognized as an effective approach to addressing class imbalance...
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Strategy of Incremental Learning on a Compartmental Spiking Neuron Model
AbstractThe article presents a method for implementing incremental learning on a compartmental spiking neuron model. The training of one neuron with...
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TLCE: Transfer-Learning Based Classifier Ensembles for Few-Shot Class-Incremental Learning
Few-shot class-incremental learning (FSCIL) struggles to incrementally recognize novel classes from few examples without catastrophic forgetting of...
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Overcomplete-to-sparse representation learning for few-shot class-incremental learning
Few-shot class-incremental learning (FSCIL) aims to continually learn new semantics given a few training samples of new classes. As training examples...
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Class-Incremental Generalized Zero-Shot Learning
Zero-Shot Learning (ZSL) focuses on transferring knowledge learned from the source domain to the target domain. In the classic setting, test data...
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iPINNs: incremental learning for Physics-informed neural networks
Physics-informed neural networks (PINNs) have recently become a powerful tool for solving partial differential equations (PDEs). However, finding a...
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Prototype Representation Expansion in Incremental Learning
Deep neural networks have made outstanding achievements in many static tasks, however, when faced with incremental scenario, they suffer from...
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Hierarchical Task-Incremental Learning with Feature-Space Initialization Inspired by Neural Collapse
Incremental learning models need to update the categories and their conceptual understanding over time. The current research has placed more emphasis...
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Generalized semi-supervised class incremental learning in presence of outliers
In this work, we focus on addressing the challenging real-world problem of generalized semi-supervised class-incremental learning (GSS-CIL), which...
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Learning a dual-branch classifier for class incremental learning
Catastrophic forgetting is a non-trivial challenge for class incremental learning, which is caused by new knowledge learning and data imbalance...
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A self-organizing incremental neural network for imbalance learning
Class imbalance learning deals with data that have very skewed class distributions, and commonly exists in real-world applications. Incremental...
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Incremental learning with neural networks for computer vision: a survey
Incremental learning is one of the most important abilities of human beings. In the age of artificial intelligence, it is the key task to make neural...
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Incremental learning without looking back: a neural connection relocation approach
Nowadays, artificial intelligence methods need to face more and more open application scenarios. They need to have the ability to continuously...
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Style creation: multiple styles transfer with incremental learning and distillation loss
Neural style transfer aims to transfer style from a style image to a content image by neural learning. A novel style will be brought if one can...
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Compositional Prompting for Anti-Forgetting in Domain Incremental Learning
Domain Incremental Learning (DIL) focuses on handling complex domain shifts of a continuous data stream for visual tasks such as image classification...
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Flexible few-shot class-incremental learning with prototype container
In the few-shot class-incremental learning, new class samples are utilized to learn the characteristics of new classes, while old class exemplars are...
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Research on flight training prediction based on incremental online learning
With the continuous development of civil aviation industry in recent years, the demand for flight training has been increasing and the flight...
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Continual prune-and-select: class-incremental learning with specialized subnetworks
The human brain is capable of learning tasks sequentially mostly without forgetting. However, deep neural networks (DNNs) suffer from catastrophic...
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FakeIDCA: Fake news detection with incremental deep learning based concept drift adaption
Social media facilitates rapid information sharing, improving exposure, connections, and content promotion. However, it also poses the challenge of...
