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Continual/Lifelong Learning with Deep Architectures, Vincenzo Lomonaco

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Humans have the extraordinary ability to learn continually from experience. Not only can we apply previously learned knowledge and skills to new situations, we can also use these as the foundation for later learning. One of the grand goals of AI is building an artificial continually learning agent that constructs a sophisticated understanding of the world from its own experience through the autonomous incremental development of ever more complex skills and knowledge.
"Continual Learning" (CL) is indeed a fast emerging topic in AI concerning the ability to efficiently improve the performance of a deep model over time, dealing with a long (and possibly unlimited) sequence of data/tasks. In this workshop, after a brief introduction of the topic, we’ll implement different Continual Learning strategies and assess them on common vision benchmarks. We’ll conclude the workshop with a look at possible real world applications of CL.

Vincenzo Lomonaco is a Deep Learning PhD student at the University of Bologna and founder of ContinualAI.org. He is also the PhD students representative at the Department of Computer Science of Engineering (DISI) and teaching assistant of the courses “Machine Learning” and “Computer Architectures” in the same department. Previously, he was a Machine Learning software engineer at IDL in-line Devices and a Master Student at the University of Bologna where he graduated cum laude in 2015 with the dissertation “Deep Learning for Computer Vision: a Comparison Between CNNs and HTMs on Object Recognition Tasks".

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Continual/Lifelong Learning with Deep Architectures, Vincenzo Lomonaco

  1. 1. Continual Learning with Deep Architectures Workshop @ Data Science Milan 28-01-2019 Vincenzo Lomonaco vincenzo.lomonaco@unibo.it PhD student @ University of Bologna Founder of ContinualAI.org
  2. 2. About me • PhD Student @ University of Bologna • Visiting Scholar @ ENSTA ParisTech • Visiting Scholar @ Purdue University • Phd Students’ Representative of the Department of Computer Science and Engineering. • Teaching Assistant of the courses Machine Learning and Computer Architectures. • Author and Technical reviewer of the online course Deep Learning with R and book R Deep Learning Essentials
  3. 3. Continual AI Community http://continualai.org https://continualai.herokuapp.com/
  4. 4. Workshop Outline 1. Introduction to Continual Learning (CL) 2. [Hands-on] A Gentle Introduction to CL in PyTorch 3. A new CL Benchmark: CORe50 4. A new CL strategy: AR1 5. Continual Unsupervised Learning 6. Continual Reinforcement Learning 7. Examples of CL applications
  5. 5. State-of-the-art • Deep Learning holds state-of-the-art performances in many tasks. • Mainly supervised training with huge and fixed datasets.
  6. 6. State-of-the-art • Deep Learning holds state-of-the-art performances in many tasks. • Mainly supervised training with huge and fixed datasets.
  7. 7. State-of-the-art • Deep Learning holds state-of-the-art performances in many tasks. • Mainly supervised training with huge and fixed datasets.
  8. 8. State-of-the-art • Deep Learning holds state-of-the-art performances in many tasks. • Mainly supervised training with huge and fixed datasets.
  9. 9. The Curse of Dimensionality # of possible 227x227 RGB images
  10. 10. The Curse of Dimensionality # of possible 227x227 RGB images
  11. 11. The Curse of Dimensionality # of possible 227x227 RGB images
  12. 12. How can we improve AI scalability and adaptability? (Hence ubiquitousness and autonomy)
  13. 13. Continual Learning
  14. 14. Continual Learning
  15. 15. Continual Learning (CL) • 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.
  16. 16. Why CL is a challenging (and fun) problem?
  17. 17. CL Strategies Architectural Regularization Rehearsal CWR PNN EWC SI LWF ICARL GEM Pure Rehearsal
  18. 18. CL Strategies Architectural Regularization Rehearsal CWR PNN EWC SI LWF ICARL GEM Pure Rehearsal
  19. 19. [Hands-on (40 minutes) ] A Gentle Introduction to CL in PyTorch https://github.com/ContinualAI/colab
  20. 20. Elastic Weights Consolidation (EWC) Fisher Information ...
  21. 21. Common CL benchmarks Dataset Strategy Permuted MNIST EWC, GEM, SI Rotated MNIST GEM MNIST Split SI CIFAR10/100 Split GEM, iCARL, SI ILSVRC2012 iCARL Atari Games EWC
  22. 22. Continual Learning needs the presence of multiple (temporal coherent and unconstrained) views of the same objects taken in different sessions. Lomonaco V. and Maltoni D. CORe50: a New Dataset and Benchmark for Continuous Object Recognition. CoRL2017. CORe50: a Video Benchmark for CL and Object Recognition/Detection
  23. 23. Lomonaco V. and Maltoni D. CORe50: a New Dataset and Benchmark for Continuous Object Recognition. CoRL2017. CORe50: a Video Benchmark for CL and Object Recognition/Detection
  24. 24. # Images 164,866 Format RGB-D Image size 350x350 128x128 # Categories 10 # Obj. x Cat. 5 # Sessions 11 # img. x Sess. ~300 # Outdoor Sess. 3 Acquisition Sett. Hand held Lomonaco V. and Maltoni D. CORe50: a New Dataset and Benchmark for Continuous Object Recognition. CoRL2017. CORe50: a Video Benchmark for CL and Object Recognition/Detection
  25. 25. Single Incremental Task 1. New Instances (NI) 2. New Classes (NC) 3. New Instances and Classes (NIC) Initial Batch Incremental Batches Τ . . .
  26. 26. CORe50 Benchmark (NI) (NC) (NIC)
  27. 27. CORe50 Website Dataset, Benchmark, code and additional information freely available at: vlomonaco.github.io/core50
  28. 28. AR-1 Combining Architectural and Regularization approaches Lomonaco V. and Maltoni D. Continuous Learning in Single-Incremental-Task Scenarios. Pre-print arxiv:1806.08568v3.
  29. 29. CL Strategies Architectural Regularization Rehearsal CWR PNN EWC SI LWF ICARL GEM Pure Rehearsal
  30. 30. CL Strategies Architectural Regularization Rehearsal CWR PNN EWC SI LWF ICARL GEM Pure Rehearsal
  31. 31. CL Strategies Architectural Regularization Rehearsal CWR PNN EWC SI LWF ICARL GEM Pure Rehearsal AR1
  32. 32. AR-1: Architectural Part ...
  33. 33. Copy Weights with Re-init (CWR) Lomonaco V. and Maltoni D. CORe50: a New Dataset and Benchmark for Continuous Object Recognition. CoRL2017. ? ? ? ? ...
  34. 34. Copy Weights with Re-init (CWR) Lomonaco V. and Maltoni D. CORe50: a New Dataset and Benchmark for Continuous Object Recognition. CoRL2017. ? ? ...
  35. 35. Copy Weights with Re-init (CWR) Lomonaco V. and Maltoni D. CORe50: a New Dataset and Benchmark for Continuous Object Recognition. CoRL2017. ? ? ...
  36. 36. Copy Weights with Re-init (CWR) Lomonaco V. and Maltoni D. CORe50: a New Dataset and Benchmark for Continuous Object Recognition. CoRL2017. ...
  37. 37. Copy Weights with Re-init (CWR) Lomonaco V. and Maltoni D. CORe50: a New Dataset and Benchmark for Continuous Object Recognition. CoRL2017. ...
  38. 38. AR-1: Regularization Part ...
  39. 39. AR-1: Additional features ...
  40. 40. AR-1 results on CORe50
  41. 41. AR-1 results on CORe50
  42. 42. AR-1 results on CORe50 AR-1
  43. 43. Unsupervised Continual Learning • “Continual Labeling” is one of greatest barrier after Catastrophic Forgetting for CL • Unsupervised Learning is where CL can really shine • Difficult to find complex tasks where Unsupervised Learning alone can suffice • What about Semi-Supervised Tuning?
  44. 44. Semi-Supervised Tuning from Temporal Coherence DL Model 0.1 0.01 0.56 0.03 0.2 0.1 0.05 0.06 0.7 0.05 0.04 0.1 Video Stream Class Probabilities Lomonaco V. and Maltoni D. Semi-Supervised Tuning from Temporal Coherence. ICPR 2016.
  45. 45. Semi-Supervised Tuning from Temporal Coherence
  46. 46. Continual Reinforcement Learning • Very interesting for futuristic Robotics applications • Too many trials needed for end-to-end learning • Yet, many possibilities for soft adaptation!
  47. 47. CRL in 3D non-stationary environment VIDEO! Lomonaco V., Desai K., Maltoni D. and Culurciello, E. Continual Reinforcement Learning in 3D non-stationary environments. To be published.
  48. 48. Environment Illumination 100% 62% 50%
  49. 49. Environment Illumination 100% 62% 50% end 01 end 02 end 03
  50. 50. Other Considered Variations Walls Textures
  51. 51. Walls Textures Objects Shape & Color Other Considered Variations
  52. 52. Continual Reinforcement Learning Objectives: • Avoid Forgetting • Improve Generalization • Speeding-up Adaptation … without “end-of-task” supervised signal!
  53. 53. Examples of CL Applications Software Engineering A Machine Learning Approach for Continuous Development. Russo Daniel, Lomonaco Vincenzo and Ciancarini Paolo. Proceedings of 5th International Conference in Software Engineering for Defense Applications, 2018.
  54. 54. Examples of CL Applications IoT Devices Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity. Carpineti Claudia, Lomonaco Vincenzo, Bedogni Luca, Di Felice Marco and Bononi Luciano. IEEE International Conference on Pervasive Computing and Communications Workshops, 2018. http://cs.unibo.it/projects/us-tm2017
  55. 55. Examples of CL Applications Drones Intelligent Drone Swarm for Search and Rescue Operations at Sea. Vincenzo Lomonaco, Angelo Trotta, Marta Ziosi, Juan de Dios Yáñez Ávila, Natalia Díaz-Rodríguez. AI for Social Good NIPS2018 Workshop.
  56. 56. Examples of CL Applications Smart Cameras Comparing Incremental Learning Strategies for Convolutional Neural Networks. Lomonaco V. and Maltoni D. IAPR Workshop on Artificial Neural Networks in Pattern Recognition. Springer International Publishing, 2016.
  57. 57. Thank you! Workshop @ Data Science Milan 28-01-2019 Vincenzo Lomonaco vincenzo.lomonaco@unibo.it PhD student @ University of Bologna Founder of ContinualAI.org

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