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Incremental Learning of Latent Forests [PDF]
IEEE Access, 2020 In the analysis of real-world data, it is useful to learn a latent variable model that represents the data generation process. In this setting, latent tree models are useful because they are able to capture complex relationships while being easily ...
Fernando Rodriguez-Sanchez +2 more
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DILS: depth incremental learning strategy [PDF]
Frontiers in Neurorobotics, 2023 There exist various methods for transferring knowledge between neural networks, such as parameter transfer, feature sharing, and knowledge distillation.
Yanmei Wang +14 more
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Uncertainty alters the balance between incremental learning and episodic memory [PDF]
eLife, 2022 A key question in decision-making is how humans arbitrate between competing learning and memory systems to maximize reward. We address this question by probing the balance between the effects, on choice, of incremental trial-and-error learning versus ...
Jonathan Nicholas +2 more
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An Appraisal of Incremental Learning Methods
Entropy, 2020 As a special case of machine learning, incremental learning can acquire useful knowledge from incoming data continuously while it does not need to access the original data.
Yong Luo +3 more
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Development and research of a neural network alternate incremental learning algorithm
Компьютерная оптика, 2023 In this paper, the relevance of developing methods and algorithms for neural network incremental learning is shown. Families of incremental learning techniques are presented. A possibility of using the extreme learning machine for incremental learning is
A.A. Orlov, E.S. Abramova
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DILRS: Domain-Incremental Learning for Semantic Segmentation in Multi-Source Remote Sensing Data
Remote Sensing, 2023 With the exponential growth in the speed and volume of remote sensing data, deep learning models are expected to adapt and continually learn over time.
Xue Rui +4 more
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Incremental learning on chip [PDF]
2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2017 Learning on chip (LOC) is a challenging problem in which an embedded system learns a model and uses it to process and classify unknown data, while adapting to new observations or classes. It may require intensive computations and complex hardware implementations to adapt to new data.
Boukli Hacene, Ghouthi +4 more
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Incremental Learning in Online Scenario [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 Modern deep learning approaches have achieved great success in many vision applications by training a model using all available task-specific data. However, there are two major obstacles making it challenging to implement for real life applications: (1) Learning new classes makes the trained model quickly forget old classes knowledge, which is referred
Jiangpeng He +3 more
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Class-Incremental Learning of Convolutional Neural Networks Based on Double Consolidation Mechanism
IEEE Access, 2020 Class-incremental learning is a model learning technique that can help classification models incrementally learn about new target classes and realize knowledge accumulation.
Leilei Jin, Hong Liang, Changsheng Yang
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Reduce the Difficulty of Incremental Learning With Self-Supervised Learning
IEEE Access, 2021 Incremental learning requires a learning model to learn new tasks without forgetting the learned tasks continuously. However, when a deep learning model learns new tasks, it will catastrophically forget tasks it has learned before.
Linting Guan, Yan Wu
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