EWGN: Elastic Weight Generation and Context Switching in Deep Learning

• URL: http://arxiv.org/abs/2506.02065v1
• Date: Sun, 01 Jun 2025 21:59:53 GMT
• Title: EWGN: Elastic Weight Generation and Context Switching in Deep Learning
• Authors: Shriraj P. Sawant, Krishna P. Miyapuram,
• Abstract summary: This paper introduces Elastic Weight Generative Networks (EWGN) as an idea for context switching between two different tasks.<n>EWGN architecture uses an additional network that generates the weights of the primary network dynamically while consolidating the weights learned.<n>Using standard computer vision datasets, namely MNIST and fashion-MNIST, we analyse the retention of previously learned task representations.
• Score: 0.6138671548064356
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ability to learn and retain a wide variety of tasks is a hallmark of human intelligence that has inspired research in artificial general intelligence. Continual learning approaches provide a significant step towards achieving this goal. It has been known that task variability and context switching are challenging for learning in neural networks. Catastrophic forgetting refers to the poor performance on retention of a previously learned task when a new task is being learned. Switching between different task contexts can be a useful approach to mitigate the same by preventing the interference between the varying task weights of the network. This paper introduces Elastic Weight Generative Networks (EWGN) as an idea for context switching between two different tasks. The proposed EWGN architecture uses an additional network that generates the weights of the primary network dynamically while consolidating the weights learned. The weight generation is input-dependent and thus enables context switching. Using standard computer vision datasets, namely MNIST and fashion-MNIST, we analyse the retention of previously learned task representations in Fully Connected Networks, Convolutional Neural Networks, and EWGN architectures with Stochastic Gradient Descent and Elastic Weight Consolidation learning algorithms. Understanding dynamic weight generation and context-switching ability can be useful in enabling continual learning for improved performance.

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