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Official implementation of "On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning"
An Extendible (General) Continual Learning Framework based on Pytorch - official codebase of Dark Experience for General Continual Learning
Implementation of "Overcoming catastrophic forgetting in neural networks" in Tensorflow
Code implementation for "Continual Semi-Supervised Learning through Contrastive Interpolation Consistency"
Steven C. Y. Hung, Jia-Hong Lee, Timmy S. T. Wan, Chein-Hung Chen, Yi-Ming Chan and Chu-Song Chen. "Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong Learning" 2019 ACM on International Conference on Multimedia Retrieval
The main features of ivclab/pae are: Continual Learning Frameworks.
Projects with overlapping indexed features include: aimagelab/lider — Official implementation of "On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning". aimagelab/mammoth — An Extendible (General) Continual Learning Framework based on Pytorch - official codebase of Dark Experience for… ariseff/overcoming-catastrophic — Implementation of "Overcoming catastrophic forgetting in neural networks" in Tensorflow. arunmallya/packnet — Code for PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning. arunmallya/piggyback — Code for Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights. aimagelab/cssl — Code implementation for "Continual Semi-Supervised Learning through Contrastive Interpolation Consistency".