FACIL

Framework for Analysis of Class-Incremental Learning with 12 state-of-the-art methods and 3 baselines.

Features

Continual Learning Frameworks - A modular framework for evaluating class-incremental learning algorithms.

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"

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"