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“Maintaining DNN Accuracy When the Real World is Changing,” a Presentation from Observa

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Edge AI and Vision Alliance
Edge AI and Vision Alliance

For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2021/10/maintaining-dnn-accuracy-when-the-real-world-is-changing-a-presentation-from-observa/ Erik Chelstad, CTO and co-founder of Observa, presents the “Maintaining DNN Accuracy When the Real World is Changing” tutorial at the May 2021 Embedded Vision Summit. We commonly train deep neural networks (DNNs) on existing data and then use the trained model to make predictions on new data. Once trained, these predictive models approximate a static mapping function from their input onto their predicted output. However, in many applications, the trained model is used on data that changes over time. In these cases, the predictive performance of these models degrades over time. In this talk, Chelstad introduces the problem of concept drift in deployed DNNs. He discusses the types of concept drift that occur in the real world, from small variances in the predicted classes all the way to the introduction of a new, previously unseen class. He also discusses approaches to recognizing these changes and identifying the point in time when it becomes necessary to update the training dataset and retrain a new model. The talk concludes with a real-world case study.

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© 2021 Observa - observanow.com Maintaining DNN Accuracy When the Real World is Changing Erik Chelstad Observa – observanow.com
© 2021 Observa - observanow.com Object Detection Using Deep Learning NNs 2
© 2021 Observa - observanow.com Defining Dataset Drift 3
Let’s Talk About Variance
© 2021 Observa - observanow.com Variance in a Dataset 5
© 2021 Observa - observanow.com Variance in Images – Easy for Humans 6
© 2021 Observa - observanow.com Environmental Variance 7
© 2021 Observa - observanow.com Planned/Expected Variance 8
© 2021 Observa - observanow.com Adding Variance 9
Drift – Time Introduces Real World Variance
© 2021 Observa - observanow.com Unplanned Variance (A Rose is a Rose is a Rose…) 11
© 2021 Observa - observanow.com Evolutionary Variance: Diversity (Phylogeny) 12
© 2021 Observa - observanow.com Evolutionary Variance: Extinction 13
Handling Variance
© 2021 Observa - observanow.com How to Detect Variations • Declining confidence • Frequency: No longer detecting objects at the same rate • Human review • You’re told about them • OCR • Pixel methods 15 Practical Difficult
© 2021 Observa - observanow.com How to Detect New Classes Systematically • Using a taxonomy that leads to an unknown endpoint • Bottle → BrandX → FlavorY → New Size? • Generalized object detection results compared to specialized models • Assuming large pixel gaps between detected objects are new classes • External context, e.g., sales data with no identified classes 16 Practical Difficult
© 2021 Observa - observanow.com When is it Time to Forget a Class? • “Forgetting” a class to have the model(s) no longer detect it as an object • Considerations • How much does it cost to retrain a model from scratch? • How many classes per model? • Are the forgotten classes now negative examples? E.g., Is the forgotten class something that is easily confused with the positive examples and makes good training data? • Cost of running object detection • Cost of maintenance (review, labeling, golden set) 17
© 2021 Observa - observanow.com When is it Time to Retrain? • Considerations • Cost to retrain model(s) • Cost to deploy models to all users • Size of changes to the dataset • Cost of getting something wrong • Cost of detecting things not cared about • Starting over or resuming from a pre-trained model 18
Case Studies
© 2021 Observa - observanow.com Case Study: Taco Shells Major Package Changes 20
© 2021 Observa - observanow.com Case Study: Taco Shells 21 New Entrants
© 2021 Observa - observanow.com Case Study: Natural Food Products Subtle Package Changes 22
In Conclusion
© 2021 Observa - observanow.com In Conclusion • Be an expert in your dataset • NNs are expensive to maintain • Know impact of your results • Get context data if you can • Have a method for review • Maintain your training/deployment methods • Find inspiration in other disciplines 24
© 2021 Observa - observanow.com Resources 25 AI Overview for CPG and Retail observa.link/ai-overview Inferring the Historical Patterns of Biological Evolution econgeography.org/faculty/pbergmann/biostats/Pagel%201999.pdf To Remember, the Brain Must Actively Forget quantamagazine.org/to-remember-the-brain-must-actively-forget-20180724/

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“Maintaining DNN Accuracy When the Real World is Changing,” a Presentation from Observa

  • 1. © 2021 Observa - observanow.com Maintaining DNN Accuracy When the Real World is Changing Erik Chelstad Observa – observanow.com
  • 2. © 2021 Observa - observanow.com Object Detection Using Deep Learning NNs 2
  • 3. © 2021 Observa - observanow.com Defining Dataset Drift 3
  • 5. © 2021 Observa - observanow.com Variance in a Dataset 5
  • 6. © 2021 Observa - observanow.com Variance in Images – Easy for Humans 6
  • 7. © 2021 Observa - observanow.com Environmental Variance 7
  • 8. © 2021 Observa - observanow.com Planned/Expected Variance 8
  • 9. © 2021 Observa - observanow.com Adding Variance 9
  • 10. Drift – Time Introduces Real World Variance
  • 11. © 2021 Observa - observanow.com Unplanned Variance (A Rose is a Rose is a Rose…) 11
  • 12. © 2021 Observa - observanow.com Evolutionary Variance: Diversity (Phylogeny) 12
  • 13. © 2021 Observa - observanow.com Evolutionary Variance: Extinction 13
  • 15. © 2021 Observa - observanow.com How to Detect Variations • Declining confidence • Frequency: No longer detecting objects at the same rate • Human review • You’re told about them • OCR • Pixel methods 15 Practical Difficult
  • 16. © 2021 Observa - observanow.com How to Detect New Classes Systematically • Using a taxonomy that leads to an unknown endpoint • Bottle → BrandX → FlavorY → New Size? • Generalized object detection results compared to specialized models • Assuming large pixel gaps between detected objects are new classes • External context, e.g., sales data with no identified classes 16 Practical Difficult
  • 17. © 2021 Observa - observanow.com When is it Time to Forget a Class? • “Forgetting” a class to have the model(s) no longer detect it as an object • Considerations • How much does it cost to retrain a model from scratch? • How many classes per model? • Are the forgotten classes now negative examples? E.g., Is the forgotten class something that is easily confused with the positive examples and makes good training data? • Cost of running object detection • Cost of maintenance (review, labeling, golden set) 17
  • 18. © 2021 Observa - observanow.com When is it Time to Retrain? • Considerations • Cost to retrain model(s) • Cost to deploy models to all users • Size of changes to the dataset • Cost of getting something wrong • Cost of detecting things not cared about • Starting over or resuming from a pre-trained model 18
  • 20. © 2021 Observa - observanow.com Case Study: Taco Shells Major Package Changes 20
  • 21. © 2021 Observa - observanow.com Case Study: Taco Shells 21 New Entrants
  • 22. © 2021 Observa - observanow.com Case Study: Natural Food Products Subtle Package Changes 22
  • 24. © 2021 Observa - observanow.com In Conclusion • Be an expert in your dataset • NNs are expensive to maintain • Know impact of your results • Get context data if you can • Have a method for review • Maintain your training/deployment methods • Find inspiration in other disciplines 24
  • 25. © 2021 Observa - observanow.com Resources 25 AI Overview for CPG and Retail observa.link/ai-overview Inferring the Historical Patterns of Biological Evolution econgeography.org/faculty/pbergmann/biostats/Pagel%201999.pdf To Remember, the Brain Must Actively Forget quantamagazine.org/to-remember-the-brain-must-actively-forget-20180724/