Continuous Learning with Deep Architectures

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One of the greatest goals of AI is building an artificial continuous learning agent which can construct a sophisticated understanding about the external world from its own experience through the adaptive, goal-oriented and incremental development of ever more complex skills and knowledge. Yet, Continuous/Lifelong Learning (CL) from high-dimensional streaming data is a challenging research problem far from being solved. In fact, fully retraining deep prediction models each time a new piece of data becomes available is infeasible, due to computational and storage issues, while naïve continuous learning strategies have been shown to suffer from catastrophic forgetting. This talk will cover some of the most common end-to-end continuous learning strategies for gradient-based architectures and the recently proposed AR-1 strategy, which can outperform other state-of-the-art regularization and architectural approaches on the CORe50 benchmark.

Datos y análisis

• Deep Learning state-of-the-art performances
• Mainly supervised training with huge and fixed datasets.

# of possible 227x227 RGB images
3,9 ∙ 10372282

• Higher and realistic time-scale where data (and tasks)
become available only during time
• No access to previously encountered data.
• Constant computational and memory resources.
• Incremental development of ever more complex
knowledge and skills.

Dataset, Benchmark, code and additional
information freely available at:
vlomonaco.github.io/core50
LomonacoV. and Maltoni D. CORe50: a New Dataset and Benchmark for ContinuousObject Recognition.CoRL2017.

Architectural
Regularization Reharshal
CWR PNN
EWC
SI
LWF
ICARL
GEM

𝑊𝑡𝑊𝑏...
𝐿0, … , 𝐿 𝑛−1 𝐿 𝑛
LomonacoV. and Maltoni D. CORe50: a New Dataset and Benchmark for ContinuousObject Recognition.CoRL2017.
?
?
?
?

𝑊𝑡𝑊𝑏...
𝐿0, … , 𝐿 𝑛−1 𝐿 𝑛
LomonacoV. and Maltoni D. CORe50: a New Dataset and Benchmark for ContinuousObject Recognition.CoRL2017.
?
?
𝑊𝑐
𝐿 𝑛

𝑊𝑡𝑊𝑏
...
𝐿0, … , 𝐿 𝑛−1 𝐿 𝑛
𝐿 𝜇 = 𝐿 𝜇 + 𝜆
𝑘
Ω 𝑘
𝜇
𝜃 𝑘 − 𝜃 𝑘
2
𝑤 𝑘
𝑣
=
𝑡 𝑣−1
𝑡 𝑣
𝜕𝐿
𝜕𝜃 𝑘
∙
𝜕𝜃 𝑘
𝜕𝑡

Combining Architectural and Regularization
approaches
LomonacoV. and Maltoni D. Continuous Learning in Single-Incremental-TaskScenarios.To be published.

Architectural
Regularization Reharshal
CWR PNN
EWC
SI
LWF
ICARL
GEM
AR1

𝑊𝑡𝑊𝑏...
𝐿0, … , 𝐿 𝑛−1 𝐿 𝑛
𝐿 𝜇 = 𝐿 𝜇 + 𝜆
𝑘
Ω 𝑘
𝜇
𝜃 𝑘 − 𝜃 𝑘
2
𝑤 𝑘
𝑣
=
𝑡 𝜇−1
𝑡 𝜇
𝜕𝐿
𝜕𝜃 𝑘
∙
𝜕𝜃 𝑘
𝜕𝑡

𝑊𝑡𝑊𝑏...
𝐿0, … , 𝐿 𝑛−1 𝐿 𝑛
• Computational efficient
(independent from the
number of training
batches)
• Just one Ω 𝑘(running sum
+ max clip)
• CWR with zero-init
• CWR with mean-shift

0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
1 2 3 4 5 6 7 8 9
Naive
LwF
EwC
Syn
CwR
AR-1
Cumulative

1. Continuous Unsupervised Learning
2. Continuous Reinforcement Learning

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2. • Deep Learning state-of-the-art performances • Mainly supervised training with huge and fixed datasets.

3. # of possible 227x227 RGB images 3,9 ∙ 10372282

4. (Hence ubiquitousness and autonomy)

5. 𝑡 𝑋1 𝑌1 𝑓𝜃: 𝑋1 → 𝑌1

6. 𝑡 𝑋2 𝑌2𝑓𝜃: 𝑋1 ∪ 𝑋2 → 𝑌1 ∪ 𝑌2

7. • Higher and realistic time-scale where data (and tasks) become available only during time • No access to previously encountered data. • Constant computational and memory resources. • Incremental development of ever more complex knowledge and skills.

9. Dataset, Benchmark, code and additional information freely available at: vlomonaco.github.io/core50 LomonacoV. and Maltoni D. CORe50: a New Dataset and Benchmark for ContinuousObject Recognition.CoRL2017.

10. Architectural Regularization Reharshal CWR PNN EWC SI LWF ICARL GEM

11. Architectural Regularization Reharshal CWR PNN EWC SI LWF ICARL GEM

12. 𝑊𝑡𝑊𝑏... 𝐿0, … , 𝐿 𝑛−1 𝐿 𝑛 LomonacoV. and Maltoni D. CORe50: a New Dataset and Benchmark for ContinuousObject Recognition.CoRL2017. ? ? ? ?

13. 𝑊𝑡𝑊𝑏... 𝐿0, … , 𝐿 𝑛−1 𝐿 𝑛 LomonacoV. and Maltoni D. CORe50: a New Dataset and Benchmark for ContinuousObject Recognition.CoRL2017. ? ? ? ?

14. 𝑊𝑡𝑊𝑏... 𝐿0, … , 𝐿 𝑛−1 𝐿 𝑛 LomonacoV. and Maltoni D. CORe50: a New Dataset and Benchmark for ContinuousObject Recognition.CoRL2017. ? ?

15. 𝑊𝑡𝑊𝑏... 𝐿0, … , 𝐿 𝑛−1 𝐿 𝑛 LomonacoV. and Maltoni D. CORe50: a New Dataset and Benchmark for ContinuousObject Recognition.CoRL2017. ? ? 𝑊𝑐 𝐿 𝑛

16. 𝑊𝑡𝑊𝑏... 𝐿0, … , 𝐿 𝑛−1 𝐿 𝑛 LomonacoV. and Maltoni D. CORe50: a New Dataset and Benchmark for ContinuousObject Recognition.CoRL2017. 𝑊𝑐 𝐿 𝑛

17. 𝑊𝑡𝑊𝑏... 𝐿0, … , 𝐿 𝑛−1 𝐿 𝑛 LomonacoV. and Maltoni D. CORe50: a New Dataset and Benchmark for ContinuousObject Recognition.CoRL2017. 𝑊𝑐 𝐿 𝑛

18. 𝑊𝑡𝑊𝑏 ... 𝐿0, … , 𝐿 𝑛−1 𝐿 𝑛 𝐿 𝜇 = 𝐿 𝜇 + 𝜆 𝑘 Ω 𝑘 𝜇 𝜃 𝑘 − 𝜃 𝑘 2 𝑤 𝑘 𝑣 = 𝑡 𝑣−1 𝑡 𝑣 𝜕𝐿 𝜕𝜃 𝑘 ∙ 𝜕𝜃 𝑘 𝜕𝑡

19. Combining Architectural and Regularization approaches LomonacoV. and Maltoni D. Continuous Learning in Single-Incremental-TaskScenarios.To be published.

20. Architectural Regularization Reharshal CWR PNN EWC SI LWF ICARL GEM

21. Architectural Regularization Reharshal CWR PNN EWC SI LWF ICARL GEM AR1

22. 𝑊𝑡 𝑊𝑐𝑊𝑏... 𝐿0, … , 𝐿 𝑛−1 𝐿 𝑛 𝐿 𝑛

23. 𝑊𝑡𝑊𝑏... 𝐿0, … , 𝐿 𝑛−1 𝐿 𝑛 𝐿 𝜇 = 𝐿 𝜇 + 𝜆 𝑘 Ω 𝑘 𝜇 𝜃 𝑘 − 𝜃 𝑘 2 𝑤 𝑘 𝑣 = 𝑡 𝜇−1 𝑡 𝜇 𝜕𝐿 𝜕𝜃 𝑘 ∙ 𝜕𝜃 𝑘 𝜕𝑡

24. 𝑊𝑡𝑊𝑏... 𝐿0, … , 𝐿 𝑛−1 𝐿 𝑛 • Computational efficient (independent from the number of training batches) • Just one Ω 𝑘(running sum + max clip) • CWR with zero-init • CWR with mean-shift

25. 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 1 2 3 4 5 6 7 8 9 Naive LwF EwC Syn CwR AR-1 Cumulative

26. 1. Continuous Unsupervised Learning 2. Continuous Reinforcement Learning

27. http://continuousai.com

28. Vincenzo Lomonaco University of Bologna

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AR-1 (Aerojet Rocketdyne) name of the booster under development in competition with the blue origin BE-4 to replace Russuan RD-180. Falcon 9 has booster B1029. united lunch alliance atals v

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