Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks. [PDF]
PLoS ONE, 2017 A long-term goal of AI is to produce agents that can learn a diversity of skills throughout their lifetimes and continuously improve those skills via experience. A longstanding obstacle towards that goal is catastrophic forgetting, which is when learning
Roby Velez, Jeff Clune
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Addressing catastrophic forgetting in payload parameter identification using incremental ensemble learning [PDF]
Frontiers in Robotics and AICollaborative robots (cobots) are increasingly integrated into Industry 4.0 dynamic manufacturing environments that require frequent system reconfiguration due to changes in cobot paths and payloads. This necessitates fast methods for identifying payload
Khaled Elgeneidy, Ali Al-Yacoub
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Catastrophic Forgetting in Deep Graph Networks: A Graph Classification Benchmark [PDF]
Frontiers in Artificial Intelligence, 2022 In this work, we study the phenomenon of catastrophic forgetting in the graph representation learning scenario. The primary objective of the analysis is to understand whether classical continual learning techniques for flat and sequential data have a ...
Antonio Carta +2 more
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Sleep prevents catastrophic forgetting in spiking neural networks by forming a joint synaptic weight representation. [PDF]
PLoS Computational Biology, 2022 Artificial neural networks overwrite previously learned tasks when trained sequentially, a phenomenon known as catastrophic forgetting. In contrast, the brain learns continuously, and typically learns best when new training is interleaved with periods of
Ryan Golden +3 more
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Model architecture can transform catastrophic forgetting into positive transfer [PDF]
Scientific Reports, 2022 The work of McCloskey and Cohen popularized the concept of catastrophic interference. They used a neural network that tried to learn addition using two groups of examples as two different tasks.
Miguel Ruiz-Garcia
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Natural Way to Overcome Catastrophic Forgetting in Neural Networks [PDF]
Современные информационные технологии и IT-образование, 2020 The problem of catastrophic forgetting manifested itself in models of neural networks based on the connectionist approach, which have been actively studied since the second half of the 20th century.
Alexey Kutalev
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Can sleep protect memories from catastrophic forgetting? [PDF]
eLife, 2020 Continual learning remains an unsolved problem in artificial neural networks. The brain has evolved mechanisms to prevent catastrophic forgetting of old knowledge during new training.
Oscar C González +4 more
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Bayesian continual learning and forgetting in neural networks [PDF]
Nature CommunicationsBiological synapses effortlessly balance memory retention and flexibility, yet artificial neural networks still struggle with the extremes of catastrophic forgetting and catastrophic remembering.
Djohan Bonnet +6 more
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Remembering for the right reasons: Explanations reduce catastrophic forgetting
Applied AI Letters, 2021 The goal of continual learning (CL) is to learn a sequence of tasks without suffering from the phenomenon of catastrophic forgetting. Previous work has shown that leveraging memory in the form of a replay buffer can reduce performance degradation on ...
Sayna Ebrahimi
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SD-IDD: Selective Distillation for Incremental Defect Detection [PDF]
SensorsSurface defects in industrial production are complex and diverse. Therefore, deep learning-based defect detection models must consistently adapt to newly emerging defect categories. The trained models generally suffer from catastrophic forgetting as they
Jing Li +3 more
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