The ContinualAI community is currently working on eight completely open-source collaborative projects: VirtualLab Wiki, Colab, Avalanche, Mailing list/Newsletter, Short-science, Paper DB and Medium Publication.
A collection of tools and resources that let researchers communicate and collaborate better remotely on a daily basis. Up to date, the ContinualAI Virtual Lab is based on Gitter and Slack, G Suit for Non Profit, a GitHub organization account, all the major social platforms, a Slack and a Twitter bot and much more.
The aim of ContinualAI Wiki is to create an open-source, collaborative wiki, a central Hub of information for Continual Learning and AI for researchers, developers and AI enthusiasts who share an interest in Continual Learning and are willing to learn more or contribute to this field.
ContinualAI Colab is a collection of notebooks and scripts (for demo, showcasing & tutorials) which can be directly imported in Google Colab and are related to Continual Learning
Avalanche is a comprehensive framework for Continual Learning Research. It aims at unifying a set of popular CL baselines, environments and benchmarks to help algorithm prototyping and experiment, with flexibility, reproducibility, efficiency and maintainability in mind. Avalanche will be based on three main modules: datasets/environments, CL baselines and evaluation metrics/protocols.
Even considering only the topic of Continual Learning, keeping up with the huge amount of papers published today can be very difficult. This is why, in this project, we plan to contribute to the awesome short-science with short descriptions of CL papers. Still to be launched.
Waiting for better AI tools for papers recommendation the ContinualAI community is maintaining a database of CL papers which we plan to release soon. It would be very rich of meta-data so that we can better navigate the incredible number of papers published each year (query example: give me the papers employing rehearsal and evaluated on CORe50). Still to be launched.
At ContinualAI we value scientific dissemination. We think that to advance science it is important to promote cutting-hedge research and make it accessible to a larger audience of people. This is why we maintain a Medium Publication where we try to distill and simplify the continual learning ideas with as little technical details as possible.
For people on-the-run, we also thought about a monthly newsletter with all the major breakthrough and news within this area (write us if you’d like to contribute or see your next article published!). We also maintain a Google group for the exchange of information within and outside the ContinualAI community.