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ETT-CKGE: Efficient Task-driven Tokens for Continual Knowledge Graph Embedding. [PDF]

Mach Learn Knowl Discov Databases
Zhu L, Lan Q, Tian Q, Sun W, Yang L, Xia L, Xie Y, Xiao X, Duan T, Tao C, Niu S.   +10 more
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SCPA-Net: Text-Enhanced Cross-Platform Framework with Semantic Consistency Enhancement for Pine Wilt Detection. [PDF]

Plants (Basel)
He S, Zhao W, Wang P, He M.
europepmc   +1 more source

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Catastrophic forgetting in connectionist networks

Trends in Cognitive Sciences, 1999
All natural cognitive systems, and, in particular, our own, gradually forget previously learned information. Plausible models of human cognition should therefore exhibit similar patterns of gradual forgetting of old information as new information is acquired.
exaly   +3 more sources

Catastrophic forgetting and mode collapse in GANs

2020 International Joint Conference on Neural Networks (IJCNN), 2020
In this paper, we show that Generative Adversarial Networks (GANs) suffer from catastrophic forgetting even when they are trained to approximate a single target distribution. We show that GAN training is a continual learning problem in which the sequence of changing model distributions is the sequence of tasks to the discriminator.
Hoang Thanh-Tung, Truyen Tran 0001
openaire   +1 more source

Avoiding Catastrophic Forgetting

Trends in Cognitive Sciences, 2017
Humans regularly perform new learning without losing memory for previous information, but neural network models suffer from the phenomenon of catastrophic forgetting in which new learning impairs prior function. A recent article presents an algorithm that spares learning at synapses important for previously learned function, reducing catastrophic ...
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Manifold learning to address catastrophic forgetting

Proceedings of the Twelfth Indian Conference on Computer Vision, Graphics and Image Processing, 2021
Prathyusha Akundi, Jayanthi Sivaswamy
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Overcoming Catastrophic Forgetting with Self-adaptive Identifiers

2018
Catastrophic forgetting is a tough issue when the agent faces the sequential multi-task learning scenario without storing previous task information. It gradually becomes an obstacle to achieve artificial general intelligence which is generally believed to behave like a human with continuous learning capability.
Fangzhou Xiong, Zhiyong Liu 0001, Xu Yang 0004   +2 more
openaire   +1 more source

Overcoming Catastrophic Forgetting in Continuous

Current convolutional neural network (CNN) models excel at image classification tasks, often achieving performance comparable to or surpassing human capabilities. However, when these models are subjected to continuous learning scenarios, where new image classes are progressively added, their accuracy on previously learned classes tends to decrease ...
Everton Lima Aleixo, Juan Gabriel Colonna   +1 more
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Mitigate Catastrophic Forgetting by Varying Goals

Proceedings of the 12th International Conference on Agents and Artificial Intelligence, 2020
Lu Chen 0006, Masayuki Murata 0001
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Pseudo-rehearsal: Achieving deep reinforcement learning without catastrophic forgetting

Neurocomputing, 2021
Brendan Mccane, Anthony Robins
exaly