Results 251 to 260 of about 151,060 (281)
Some of the next articles are maybe not open access.
Catastrophic forgetting in connectionist networks
Trends in Cognitive Sciences, 1999All 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 in Continual Concept Bottleneck Models
Lecture Notes in Computer Science, 2022 Almost all Deep Learning models are dramatically affected by Catastrophic Forgetting when learning over continual streams of data. To mitigate this problem, several strategies for Continual Learning have been proposed, even though the extent of the ...
Simone Calderara +2 more
exaly +2 more sources
Understanding catastrophic forgetting for adaptive deep learning
Proceedings of the 6th Joint International Conference on Data Science & Management of Data (10th ACM IKDD CODS and 28th COMAD), 2023 Deep learning is still limited in practice tho it has progressed state of the art over the past few years. Current deep learning algorithms are rigid and static once trained and can't adapt to new data when deployed for inferencing.
Sawant, Shriraj Pramod
openaire +3 more sources
Neural Networks, 2020 Continual learning, a widespread ability in people and animals, aims to learn and acquire new knowledge and skills continuously. Catastrophic forgetting usually occurs in continual learning when an agent attempts to learn different tasks sequentially ...
Hong Qiao, Fangzhou Xiong, Kaizhu Huang
exaly +2 more sources
Avoiding Catastrophic Forgetting
Trends in Cognitive Sciences, 2017Humans 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 ...
openaire +2 more sources
Manifold learning to address catastrophic forgetting
Proceedings of the Twelfth Indian Conference on Computer Vision, Graphics and Image Processing, 2021Prathyusha Akundi, Jayanthi Sivaswamy
openaire +2 more sources
A Study on Catastrophic Forgetting in Deep LSTM Networks
2019We present a systematic study of Catastrophic Forgetting (CF), i.e., the abrupt loss of previously acquired knowledge, when retraining deep recurrent LSTM networks with new samples. CF has recently received renewed attention in the case of feed-forward DNNs, and this article is the first work that aims to rigorously establish whether deep LSTM networks
Monika Schak, Alexander Gepperth
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 +1 more
openaire +1 more source
Mitigate Catastrophic Forgetting by Varying Goals
Proceedings of the 12th International Conference on Agents and Artificial Intelligence, 2020Lu Chen 0006, Masayuki Murata 0001
openaire +2 more sources
Pseudo-rehearsal: Achieving deep reinforcement learning without catastrophic forgetting
Neurocomputing, 2021Brendan McCane +2 more
exaly