CSSL @ IJCAI 2021: The First International Workshop on Continual Semi-Supervised Learning Montreal, Canada, August 21-23, 2021 |
Conference website | https://sites.google.com/view/sscl-workshop-ijcai-2021/ |
Submission link | https://easychair.org/conferences/?conf=csslijcai2021 |
CSSL @ IJCAI 2021 is the first edition of the Continual Semi-Supervised Learning workshop to be held in conjunction with IJCAI 2021.
The aim of this workshop is to formalise a new continual semi-supervised learning paradigm and to introduce it to the machine learning community in order to mobilise effort in this direction. We will present the first two benchmark datasets designed for this problem, derived from important computer vision scenarios, and propose the first Continual Semi-Supervised Learning Challenges to the research community.
Submission Guidelines
Submitted papers on the topic will follow the standard IJCAI 2021 template.
Authors are welcome to submit a supplementary material document with details on their implementation; however, reviewers are not required to consult this additional material when assessing the submission.
The Workshop will allow for the submission of papers concurrently submitted elsewhere, with the aim of aggregating all relevant efforts in this area.
Double-blind review: Authors must not include any identifying information (names, affiliations, etc.) or links and self-references that may reveal their identities.
The organisers aim to provide feedback from three reviewers per submission, which will assess the submission based on relevance, novelty and potential for impact. Reviewers are asked to assess the submission (Reject/Borderline/Accept) as well as provide written feedback. There will be no additional rebuttal period.
The authors of accepted papers must guarantee their presence at the workshop. At least one author for each accepted paper must register for the conference. The same holds for Challenge winners.
List of Topics
We invite papers on continual learning in its broader sense, covering for instance the following topics:
- Analysis of suitability of existing datasets for continual learning
- New benchmark datasets explicitly designed for continual learning
- Protocols for training and testing in different continual learning settings
- Metrics for assessing continual learning methods
- Task-based continual learning
- Relation between continual learning and model adaptation
- Learning of new classes as opposed to learning from new instances
- Real-world applications of continual learning
- Catastrophic forgetting and mitigation strategies
- Applications of transfer learning, multi-task and meta-learning to continual learning
- Continual supervised, semi-supervised and unsupervised learning
- Lifelong, few-shot learning
- Continual reinforcement and inverse reinforcement learning
The list is in no way exhaustive, as the aim is to foster the debate around all aspects of continual learning, especially those which are subject of ongoing frontier research.
Committees
Program Committee
- TBD
Organizing committee
- Fabio Cuzzolin (Oxford Brookes University)
- Kevin Cannons (Huawei Technologies Canada)
- Vincenzo Lomonaco (University of Pisa and ContinualAI)
- Irina Rish (University of Montreal and MILA)
- Salman Khan (Oxford Brookes University)
- Mohamad Asiful Hossain (Huawei Technologies Canada)
- Ajmal Shahbaz (Oxford Brookes University)
Invited Speakers
- Razvan Pascanu (Deepmind)
- Tinne Tuytelaars (KU Leuven)
- Chelsea Finn (Stanford)
- Bing Liu (University of Illinois at Chicago)
Publication
CSSL @ IJCAI 2021 proceedings will be published by a professional scientific publisher. The details will be provided soon.
Venue
The conference will be held virtually. Details on the video conference tool and intructions for participants will be issued in due time.
Contact
All questions about submissions should be emailed to fabio.cuzzolin@brookes.ac.uk