Results 21 to 30 of about 24,131 (257)
Representational Continuity for Unsupervised Continual Learning
, 2021 Continual learning (CL) aims to learn a sequence of tasks without forgetting the previously acquired knowledge. However, recent CL advances are restricted to supervised continual learning (SCL) scenarios. Consequently, they are not scalable to real-world applications where the data distribution is often biased and unannotated. In this work, we focus on
Divyam Madaan +4 more
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Proceedings of the AAAI Conference on Artificial Intelligence, 2020 We propose a novel continual learning method called Residual Continual Learning (ResCL). Our method can prevent the catastrophic forgetting phenomenon in sequential learning of multiple tasks, without any source task information except the original network.
Janghyeon Lee 0001 +3 more
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Heterogeneous Continual Learning
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 Accepted to CVPR ...
Divyam Madaan +4 more
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, 2020 Continual lifelong learning requires an agent or model to learn many sequentially ordered tasks, building on previous knowledge without catastrophically forgetting it. Much work has gone towards preventing the default tendency of machine learning models to catastrophically forget, yet virtually all such work involves manually-designed solutions to the ...
Shawn Beaulieu +6 more
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Adversarial Continual Learning [PDF]
, 2020 Accepted at ECCV ...
Sayna Ebrahimi +4 more
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Continual Learning for Steganalysis
CoRR, 2022 To detect the existing steganographic algorithms, recent steganalysis methods usually train a Convolutional Neural Network (CNN) model on the dataset consisting of corresponding paired cover/stego-images. However, it is inefficient and impractical for those steganalysis tools to completely retrain the CNN model to make it effective against both the ...
Zihao Yin, Ruohan Meng, Zhili Zhou 0001
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Variational Continual Learning
CoRR, 2017 Published at International Conference on Learning Representations (ICLR ...
Turner, RE +3 more
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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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A review of continual learning for robotics
智能科学与技术学报, 2022 One of the limitations of robotics is that it is difficult for robots to adapt to fickle tasks.A robot will inevitably forget the knowledge from old environments or tasks when facing new environments or tasks.In order to summarize research in continual ...
Chao ZHAO +4 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
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