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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.

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Continuous Learning with Deep Architectures

  1. 1. Vincenzo Lomonaco University of Bologna
  2. 2. • Deep Learning state-of-the-art performances • Mainly supervised training with huge and fixed datasets.
  3. 3. # of possible 227x227 RGB images 3,9 ∙ 10372282
  4. 4. (Hence ubiquitousness and autonomy)
  5. 5. 𝑡 𝑋1 𝑌1 𝑓𝜃: 𝑋1 → 𝑌1
  6. 6. 𝑡 𝑋2 𝑌2𝑓𝜃: 𝑋1 ∪ 𝑋2 → 𝑌1 ∪ 𝑌2
  7. 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.
  8. 8. 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.
  9. 9. Architectural Regularization Reharshal CWR PNN EWC SI LWF ICARL GEM
  10. 10. Architectural Regularization Reharshal CWR PNN EWC SI LWF ICARL GEM
  11. 11. 𝑊𝑡𝑊𝑏... 𝐿0, … , 𝐿 𝑛−1 𝐿 𝑛 LomonacoV. and Maltoni D. CORe50: a New Dataset and Benchmark for ContinuousObject Recognition.CoRL2017. ? ? ? ?
  12. 12. 𝑊𝑡𝑊𝑏... 𝐿0, … , 𝐿 𝑛−1 𝐿 𝑛 LomonacoV. and Maltoni D. CORe50: a New Dataset and Benchmark for ContinuousObject Recognition.CoRL2017. ? ? ? ?
  13. 13. 𝑊𝑡𝑊𝑏... 𝐿0, … , 𝐿 𝑛−1 𝐿 𝑛 LomonacoV. and Maltoni D. CORe50: a New Dataset and Benchmark for ContinuousObject Recognition.CoRL2017. ? ?
  14. 14. 𝑊𝑡𝑊𝑏... 𝐿0, … , 𝐿 𝑛−1 𝐿 𝑛 LomonacoV. and Maltoni D. CORe50: a New Dataset and Benchmark for ContinuousObject Recognition.CoRL2017. ? ? 𝑊𝑐 𝐿 𝑛
  15. 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. 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. 17. 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
  18. 18. Combining Architectural and Regularization approaches LomonacoV. and Maltoni D. Continuous Learning in Single-Incremental-TaskScenarios.To be published.
  19. 19. Architectural Regularization Reharshal CWR PNN EWC SI LWF ICARL GEM
  20. 20. Architectural Regularization Reharshal CWR PNN EWC SI LWF ICARL GEM AR1
  21. 21. 𝑊𝑡 𝑊𝑐𝑊𝑏... 𝐿0, … , 𝐿 𝑛−1 𝐿 𝑛 𝐿 𝑛
  22. 22. 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
  23. 23. 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
  24. 24. 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
  25. 25. 1. Continuous Unsupervised Learning 2. Continuous Reinforcement Learning
  26. 26. http://continuousai.com
  27. 27. Vincenzo Lomonaco University of Bologna

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