This presentation provides an introduction to continual learning, including definitions of key terms, challenges, and potential solutions. It defines continual learning as learning sequentially while avoiding forgetting previously learned classes without access to old data. The main challenge is catastrophic forgetting. Proposed solutions include memory/rehearsal techniques, regularization methods, architectural approaches, and meta-learning. Benchmarks are discussed to evaluate how well methods work, with most real-world applications prioritizing performance, speed, and memory constraints over continual learning abilities. Recent challenges show pretrained models and memory-based techniques have had success in industry applications.

1. Study Meeting Presentation:
(A very quick introduction) Continual Learning
Author: Aurélie Peng
Date: 2021/10/27

2. What is Continual Learning ?
• Learn sequentially.
• Try not to forget previously learned classes
• Old data are not available when training new classes
Sorry, this is very simple introduction by a very beginner-level person

3. Terminology
Lifelong
Learning
Reinforcement
Learning
Online
Learning
Incremental
Learning
Continual
Learning

4. Terminology
“Task” = Learn some classes at once.
Example :
Task 1. “Cat”, “Dog”, “Bird”
Task 2. “House”, “Building”
Task 3. “Apple”, “Orange”, “Pear”

5. Why it’s useful
- Memory : Don’t need to store all data, only newest one
- Speed : Don’t have to train from scratch everytime there’s new data /
task.
- Ex. If client wants to add new class to model but only give you one hour and 100 yens budget …..

7. Catastrophic forgetting or difficult question ?
Shiba vs. Drink Shiba vs. Akita

8. Main methods against catastrophic forgetting
•Memory and rehearsal
•Regularization
•Architectural method
•(Meta-Learning)

9. Memory and rehearsal
- Store samples from previous tasks
- When training on new tasks,
sometimes use samples from
memory
“Standard” memory size
- ImageNet : About 20 images / class
- Cifar10 : About 10-20 images / class
“On Tiny Episodic Memories in Continual Learning” A. Chaudhry
https://arxiv.org/abs/1902.10486

10. Regularization
Add constraints to protect
the important weights from
previous tasks
Example. Important weights
can not be changed too much
Many other methods exists like Distillation Loss :
“Learning without Forgetting”, Z. Li, https://arxiv.org/pdf/1606.09282.pdf

11. Architectural method
• Add more parameters to
the architecture
• Often used with
multitask (if know task,
can choose which
parameters to use)

12. (Meta-learning)
• Many complicated methods exist ….
• “Learn to learn”
• Backpropagation on the backpropagation ….
• Example 1. Learn to learn continually
“Meta-Learning Representations for Continual Learning”, K. Javed
“https://arxiv.org/abs/1905.12588
• Example 2. Learn to select “important” features
“Learning to Continually Learn”, S. Beaulieu,
https://arxiv.org/abs/2002.09571

13. Benchmarks – Does it work well ?
Continual Learning with
nothing against catastrophic
forgetting
“Normal” training
Not continual
Source :
“Dark Experience for General
Continual Learning: a Strong,
Simple Baseline”, P. Buzzega
https://arxiv.org/pdf/2004.072
11.pdf

14. In the Industry …
• What matter are : Performance, Speed, Memory
• Example :
Goal : Make robots learn by showing pictures of object :
- Low computational capacity
- Can’t store many previously seen images

15. CVPR 2020 challenge on Continual Learning
• Dataset for Object classification
- 50 classes (about 300 images per class)
- 11 environments
- Fine-grained
https://sites.google.com/view/clvision2020/challenge

16. CVPR 2020 challenge on Continual Learning
• 3 scenarios
1. Each step = All classes in one environment
2. Each step = Some classes in all environment
3. Each step = One class in one environment
• Metrics
Accuracy on Test, Average Accuracy on Validation,
Training Time, Test Time,
RAM, Disk Usage
https://sites.google.com/view/clvision2020/challenge

17. CVPR 2020 challenge on Continual Learning
• Results
Source https://arxiv.org/abs/2009.09929

18. What seems to work in the industry ?
Finalists of the challenge
• All used pretrained model (biggest factor)
• Majority used memory
• One person used regularization
Research world : Almost no paper use pretrained model.
There might currently be a big gap between research & industry …

19. End !