Results 41 to 50 of about 33,100 (246)
Overcoming Catastrophic Forgetting With Unlabeled Data in the Wild [PDF]
2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019 ICCV 2019; v3 updated Figure ...
Kibok Lee 0003 +3 more
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How catastrophic can catastrophic forgetting be in linear regression?
CoRR, 2022 To better understand catastrophic forgetting, we study fitting an overparameterized linear model to a sequence of tasks with different input distributions. We analyze how much the model forgets the true labels of earlier tasks after training on subsequent tasks, obtaining exact expressions and bounds. We establish connections between continual learning
Itay Evron +4 more
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Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition
, 2020 We introduce a probabilistic approach to unify open set recognition with the prevention of catastrophic forgetting in deep continual learning, based on variational Bayesian inference.
Hong, Yong Won +4 more
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Incremental Learning With Adaptive Model Search and a Nominal Loss Model
IEEE Access, 2022 This paper addresses an incremental learning problem, in which tasks are learned sequentially without access to the previously trained dataset. Catastrophic forgetting is a significant bottleneck to incremental learning as the network performs poorly on ...
Chanho Ahn, Eunwoo Kim, Songhwai Oh
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Continual Learning Objective for Analyzing Complex Knowledge Representations
Sensors, 2022 Human beings tend to incrementally learn from the rapidly changing environment without comprising or forgetting the already learned representations. Although deep learning also has the potential to mimic such human behaviors to some extent, it suffers ...
Asad Mansoor Khan +4 more
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On the role of neurogenesis in overcoming catastrophic forgetting
CoRR, 2018 Lifelong learning capabilities are crucial for artificial autonomous agents operating on real-world data, which is typically non-stationary and temporally correlated. In this work, we demonstrate that dynamically grown networks outperform static networks in incremental learning scenarios, even when bounded by the same amount of memory in both cases ...
German Ignacio Parisi +2 more
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Investigating Catastrophic Forgetting of Deep Learning Models Within Office 31 Dataset
IEEE AccessDeep learning models have shown impressive performance in various tasks. However, they are prone to a phenomenon called catastrophic forgetting. This means they do not remember what they have learned when training on new tasks. In this research paper, we
Hidayaturrahman +3 more
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Catastrophic Importance of Catastrophic Forgetting
CoRR, 2018 This paper describes some of the possibilities of artificial neural networks that open up after solving the problem of catastrophic forgetting. A simple model and reinforcement learning applications of existing methods are also proposed.
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Overcoming Catastrophic Forgetting in Graph Neural Networks
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 Catastrophic forgetting refers to the tendency that a neural network ``forgets'' the previous learned knowledge upon learning new tasks. Prior methods have been focused on overcoming this problem on convolutional neural networks (CNNs), where the input samples like images lie in a grid domain, but have largely overlooked graph neural networks (GNNs ...
Huihui Liu, Yiding Yang, Xinchao Wang
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Neural modularity helps organisms evolve to learn new skills without forgetting old skills. [PDF]
PLoS Computational Biology, 2015 A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new ...
Kai Olav Ellefsen +2 more
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