Results 11 to 20 of about 337,942 (263)
Applied Sciences, 2023 This paper proposes a supervised classification algorithm capable of continual learning by utilizing an Adaptive Resonance Theory (ART)-based growing self-organizing clustering algorithm.
Naoki Masuyama +3 more
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Loss of plasticity in deep continual learning. [PDF]
NatureDohare S +5 more
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CLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification
Sensors, 2020 Remote sensing image scene classification has a high application value in the agricultural, military, as well as other fields. A large amount of remote sensing data is obtained every day. After learning the new batch data, scene classification algorithms
Haifeng Li +6 more
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Continual Barlow Twins: Continual Self-Supervised Learning for Remote Sensing Semantic Segmentation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 In the field of earth observation (EO), continual learning (CL) algorithms have been proposed to deal with large datasets by decomposing them into several subsets and processing them incrementally.
Valerio Marsocci, Simone Scardapane
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Extensible Steganalysis via Continual Learning
Fractal and Fractional, 2022 To realize secure communication, steganography is usually implemented by embedding secret information into an image selected from a natural image dataset, in which the fractal images have occupied a considerable proportion.
Zhili Zhou +3 more
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Towards Continual Reinforcement Learning through Evolutionary Meta-Learning [PDF]
, 2019 In continual learning, an agent is exposed to a changing environment, requiring it to adapt during execution time. While traditional reinforcement learning (RL) methods have shown impressive results in various domains, there has been less progress in ...
Grbic, Djordje, Risi, Sebastian
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Hebbian Continual Representation Learning
Proceedings of the Annual Hawaii International Conference on System Sciences, 2023 Continual Learning aims to bring machine learning into a more realistic scenario, where tasks are learned sequentially and the i.i.d. assumption is not preserved. Although this setting is natural for biological systems, it proves very difficult for machine learning models such as artificial neural networks.
Morawiecki, Pawel +3 more
openaire +4 more sources
Homeostasis-Inspired Continual Learning: Learning to Control Structural Regularization
IEEE Access, 2021 Learning continually without forgetting might be one of the ultimate goals for building artificial intelligence (AI). However, unless there are enough resources equipped, forgetting knowledge acquired in the past is inevitable.
Joonyoung Kim +3 more
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智能科学与技术学报, 2022 Large-scale pre-training models have achieved great success in the field of natural language processing by using large-scale corpora and pre-training tasks.With the gradual development of large models, the continual learning ability of large models has ...
Yue YU +5 more
doaj
Visual Tracking by Adaptive Continual Meta-Learning
IEEE Access, 2022 We formulate the visual tracking problem as a semi-supervised continual learning problem, where only an initial frame is labeled. In contrast to conventional meta-learning based approaches that regard visual tracking as an instance detection problem with
Janghoon Choi +4 more
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