IJCAI 2021
International Workshop on Continual Semi-Supervised Learning
First Edition
August 19-20 2021 | Montreal, Canada (Virtual)
Introduction
Whereas continual learning has recently attracted much attention in the machine learning community, the focus has been mainly on preventing the model updated in the light of new data from ‘catastrophically forgetting’ its initial knowledge and abilities. This, however, is in stark contrast with common real-world situations in which an initial model is trained using limited data, only to be later deployed without any additional supervision. In these scenarios the goal is for the model to be incrementally updated using the new (unlabelled) data, in order to adapt to a target domain continually shifting over time.
The aim of this workshop is to formalise this new continual semi-supervised learning (CSSL) paradigm, and to introduce it to the machine learning community in order to mobilise effort in this direction. We present two new benchmark datasets for this problem and propose a number of challenges to the research community.
Topics of the Workshop
The goal of this workshop is to propose to the research community in artificial intelligence and machine learning the new continual semi-supervised learning problem. At the same time, we will accept papers on continual learning in its broader interpretation, covering for instance the following topics:
Analysis of suitability of existing datasets for continual learning.
New benchmark datasets explicitly designed for continual learning settings
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. We invite both paper track contributions on these topics, as well as submissions of entries to a set of challenges specifically designed to test CSSL approaches.
News and Updates
March 15: The International Workshop on Continual Semi-Supervised Learning is accepted @ IJCAI 2021!
March 25: Initial version of the website is online.
April 1st: Challenges open for registration
Program Schedule
The workshop will be held in virtual space (Red Room #4). The details are sent via email. The assigned posters in the sessions are as follows:
Poster Session #1:
1) Unsupervised Continual Learning Via Pseudo Labels
2) Transfer and Continual Supervised Learning for Robotic Grasping through Grasping Features
3) Unsupervised Continual Learning via Self-Adaptive Deep Clustering Approach
4) Evaluating Continual Learning Algorithms by Generating 3D Virtual Environments
5) A Benchmark and Empirical Analysis for Replay Methods in Continual Learning
Poster Session #2:
1) Distilled Replay: Overcoming Forgetting through Synthetic Samples
2) Self-supervised Novelty Detection for Continual Learning: A Gradient-based Approach Boosted by Binary Classification
3) Self-Improving Semantic Perception for Indoor Localisation
4) SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
5) International Workshop on Continual Semi-Supervised Learning: Introduction, Benchmarks and Baselines
6) Hypernetworks for Continual Semi-Supervised Learning
Timelines
Challenges
Challenges open for registration: April 1 2021
Training and validation fold release: May 5 2021
Test fold release: June 30 2021
Submission of results: July 29 2021
Announcement of results: July 30 2021
Workshop: August 19-20 2021 (UK Afternoon/Evenings)
Precise schedule would be announced later
Paper Track
Paper submission: July 2 2021
Author notification: July 19 2021
Camera-ready submission: July 31 2021
Accepted Papers:
Unsupervised Continual Learning Via Pseudo Labels
Transfer and Continual Supervised Learning for Robotic Grasping through Grasping Features
Unsupervised Continual Learning via Self-Adaptive Deep Clustering Approach
Evaluating Continual Learning Algorithms by Generating 3D Virtual Environments
A Benchmark and Empirical Analysis for Replay Methods in Continual Learning
SPeCiaL: Self-Supervised Pretraining for Continual Learning
Distilled Replay: Overcoming Forgetting through Synthetic Samples
Hypernetworks for Continual Semi-Supervised Learning
Self-supervised Novelty Detection for Continual Learning: A Gradient-based Approach Boosted by Binary Classification
Self-Improving Semantic Perception for Indoor Localisation
SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
International Workshop on Continual Semi-Supervised Learning: Introduction, Benchmarks and Baselines
Invited Speakers
Razvan Pascanu
Deepmind
Tinne Tuytelaars
KU Leuven
Chelsea Finn
Stanford
Bing Liu
University of Illinois at Chicago
Organisers
Fabio Cuzzolin (Oxford Brookes University, Oxford, UK): fabio.cuzzolin@brookes.ac.uk
Irina Rish (University of Montreal and MILA, Canada): irina.rish@gmail.com
Kevin Cannons (Huawei Technologies Canada, Vancouver, Canada): kevin.cannons@huawei.com
Vincenzo Lomonaco (University of Pisa, Italy): vincenzo.lomonaco@unipi.it
Mohammad Asiful Hossain (Huawei Technologies Canada): mohammad.asiful.hossain@huawei.com
Salman Khan (Oxford Brookes University, Oxford, UK): 19052999@brookes.ac.uk
Ajmal Shahbaz (Oxford Brookes University, Oxford, UK): ashahbaz@brookes.ac.uk