Continual \lifelong learning - model

The continual learning or lifelong learning approach represents an effective way of advancing machine learning techniques to ensure efficient, adaptable, and intelligent handling of tasks in which knowledge must be maintained on a continual basis. The approach. represents task learning in sequence. and a mechanism of learning in such a way that knowledge gained is not lost while mastering. new. tasks. Inherent adaptability to changes in the environment makes. the model immune to catastrophes. Through. the combination of memory consolidation, replay, and regularization approaches, the.. continual learning method enables effective learning. It represents a lightweight and scalable approach. that can be applied to. robotic systems and adaptive recommendation systems. Furthermore, this project is indicative. of the criticality. of lifelong learning schemes in the. contemporary digital age, wherein adaptability, optimization, and knowledge transfer play a.. vital role in the present-day digital scenario. The suggested project takes into account the aspect of adaptability to real-time. tasks and. the preservation of knowledge, thereby contributing towards building intelligent. environments through minimal repetitions of training with the help. of the human expert. due to adaptability. being used for applications in other areas as well.

1.Elastic Weight Consolidation (EWC)(Based on Kirkpatrick et al. 2017): Think of a neural network like a brain that has learned a specific skill. EWC works by figuring out which connections. 2. Learning without Forgetting (LwF) (Based on Li & Hoiem 2017): The main advantage of this method is that it doesn't need to save old data (which saves memory). 3. Gradient Episodic Memory (GEM) (Based on Lopez-Paz & Ranzato 2017): GEM is different because it actually keeps a small "episodic memory"—a tiny buffer of data from previous tasks. 4. Progressive Neural Networks (Based on Rusu et al. 2016): This approach takes a brute-force method to stop forgetting: it simply never touches the old parameters. 5. Shin H. Lee J. K. Kim J. & Kim J. (2017). Continual learning with deep generative replay. Advances in Neural Information Processing Systems (NeurIPS) 30. 6. Mallya A. & Lazebnik S. (2018). PackNet: Adding multiple tasks to a single network by iterative pruning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 7765-7773. 7. Aljundi R. Babiloni F. Ebrahimi J. & Tuytelaars T. (2018). Memory aware synapses: Learning what (not) to forget. Proceedings of the European Conference on Computer Vision (ECCV) 139-154. 8. Aljundi R. Babiloni F. Ebrahimi J. & Tuytelaars T. (2018). Memory aware synapses: Learning what (not) to forget. Proceedings of the European Conference on Computer Vision (ECCV) 139-154. 9. Buzzega P. Boschini M. Porrello A. Abati D. & Calderara S. (2020). Dark experience for general continual learning: a strong simple baseline. Advances in Neural Information Processing Systems (NeurIPS) 33 15920-15930.