Results 11 to 20 of about 151,060 (281)
Overcoming Catastrophic Forgetting by XAI [PDF]
CoRR, 2022 Explaining the behaviors of deep neural networks, usually considered as black boxes, is critical especially when they are now being adopted over diverse aspects of human life. Taking the advantages of interpretable machine learning (interpretable ML), this work proposes a novel tool called Catastrophic Forgetting Dissector (or CFD) to explain ...
Nguyen, Giang
core +5 more sources
The Importance of Robust Features in Mitigating Catastrophic Forgetting [PDF]
2023 IEEE Symposium on Computers and Communications (ISCC), 2023 Continual learning (CL) is an approach to address catastrophic forgetting, which refers to forgetting previously learned knowledge by neural networks when trained on new tasks or data distributions. The adversarial robustness has decomposed features into robust and non-robust types and demonstrated that models trained on robust features significantly ...
Hikmat Khan +2 more
core +5 more sources
Measuring Catastrophic Forgetting in Neural Networks
Proceedings of the AAAI Conference on Artificial Intelligence, 2018 Deep neural networks are used in many state-of-the-art systems for machine perception. Once a network is trained to do a specific task, e.g., bird classification, it cannot easily be trained to do new tasks, e.g., incrementally learning to recognize additional bird species or learning an entirely different task such as flower ...
Ronald Kemker +4 more
openaire +5 more sources
How catastrophic can catastrophic forgetting be in linear regression? [PDF]
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
core +6 more sources
Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization [PDF]
Proceedings of the National Academy of Sciences of the United States of America, 2018 David J. Freedman +2 more
exaly +2 more sources
Do not Forget to Attend to Uncertainty while Mitigating Catastrophic Forgetting [PDF]
2021 IEEE Winter Conference on Applications of Computer Vision (WACV), 2021 One of the major limitations of deep learning models is that they face catastrophic forgetting in an incremental learning scenario. There have been several approaches proposed to tackle the problem of incremental learning. Most of these methods are based on knowledge distillation and do not adequately utilize the information provided by older task ...
Vinod K. Kurmi +3 more
openaire +3 more sources
Variational Continuous Bayesian Meta-learning Based Algorithm for Recommendation [PDF]
Jisuanji kexue, 2023 Meta-learning methods have been introduced into recommendation algorithms in recent years to alleviate the problem of cold start.The existing meta-learning algorithms can only improve the ability of the algorithm to deal with a set of statically ...
ZHU Wentao, LIU Wei, LIANG Shangsong, ZHU Huaijie, YIN Jian
doaj +1 more source
Incremental Fault Diagnosis Method Based on Metric Feature Distillation and Improved Sample Memory
IEEE Access, 2023 Incremental learning-based fault diagnosis systems (IFD) are widely used because of their ability to handle constantly updated fault data and types. However, the catastrophic forgetting problem remains the most crucial contemporary challenge facing IFD ...
Qilang Min +3 more
doaj +1 more source
On Sequential Bayesian Inference for Continual Learning
Entropy, 2023 Sequential Bayesian inference can be used for continual learning to prevent catastrophic forgetting of past tasks and provide an informative prior when learning new tasks.
Samuel Kessler +4 more
doaj +1 more source
Alleviating Catastrophic Forgetting in Continual Learning [PDF]
, 2022 Machine learning has enjoyed rapid and substantial advances in the past few years. However, machine learning models cannot learn continually as we humans do. Humans are continual learners, meaning they can accumulate knowledge, use the previous knowledge
Mirzadeh, Seyed Iman
core +1 more source