What are some strategies to prevent catastrophic forgetting in ANN models?
Catastrophic forgetting is a problem that affects artificial neural network (ANN) models when they learn new tasks and forget the previous ones. This can limit the ability of ANNs to perform lifelong or continual learning, which is essential for many real-world applications. In this article, you will learn what causes catastrophic forgetting, how to measure it, and what are some strategies to prevent or mitigate it.
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Dharunkumar Senthilkumar| Robotics, Education, AI | MSc MPSYS at Chalmers University | Open to internships and projects |
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Cmdr (Dr.?) Reji Kurien Thomas , FRSA, MLE?I Empower Sectors as a Global Tech & Business Transformation Quantum Leader| Stephen Hawking Award 2024| Harvard Leader…
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Nagesh Singh ChauhanDirector - Data Science and Pricing at OYO