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Problems with CNNs and Introduction to capsule neural networks

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Explains the problems with ConvNets and Introduces Capsule Neural Networks in simple words.

References and Further reading -
1. https://arxiv.org/abs/1609.08758
2. https://arxiv.org/abs/1710.08864
3. https://arxiv.org/abs/1710.09829v1
4. https://medium.com/mlreview/deep-neural-network-capsules-137be2877d44

Thanks to -
https://www.youtube.com/watch?v=VKoLGnq15RM&t=1099s

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Problems with CNNs and Introduction to capsule neural networks

  1. 1. Problems with CNNs and Introduction to Capsule Neural Networks Vipul Vaibhaw
  2. 2. Talk Overview ● Why Neural Networks? ● Introduction to Convolutional Neural Networks ● The Problems with Convolutional Neural Networks ● Introduction to Capsule Neural Networks
  3. 3. Why Neural Networks? 1. Different approach than conventional algorithmic approach 2. Great for identifying patterns. 3. More generalized solutions. 4. Neural networks and conventional algorithmic computers are not in competition but complement each other.
  4. 4. Convolutional Neural Networks In machine learning, a convolutional neural network (CNN, or ConvNet) is a class of deep, feed-forward artificial neural networks that has successfully been applied to analyzing visual imagery.
  5. 5. Applications 1. Image recognition 2. Video Analysis 3. Natural language processing 4. Checkers 5. Go many more...
  6. 6. Demo Video Summarization using Deep Semantics Features Research Paper - https://arxiv.org/abs/1609.08758
  7. 7. The Problems with ConvNets
  8. 8. Let’s look at the architecture
  9. 9. Anybody can implement ConvNets
  10. 10. Cannot extrapolate understanding of Geometry! SEVEN ??
  11. 11. No Relationship b/w nose and mouth! Sub-sampling/Pooling of the images loses the relationship between higher level parts such as Nose and Mouth. These spatial relationship are very much needed for identity recognition This is not a face!
  12. 12. Equivariance v/s Invariance ● Pooling is invariant to small changes in viewpoints. ● Equivariance - Changes in viewpoints leads to corresponding changes in Neural Networks.
  13. 13. Fooling Deep Neural Nets! ● Deep neural networks(DNN) is not continuous and very sensitive to tiny perturbation on the input vectors. ● CNN perform poorly when there is Noise in the image. ● Say, changing 1 pixel in an image won’t have any effect on a picture of Cat to a human being but when this attack was carried on a DNN its confidence dropped from 98.7% to 73.8% Link to the research paper - https://arxiv.org/abs/1710.08864
  14. 14. Capsule Neural Networks!
  15. 15. Capsule Neural Networks
  16. 16. What is a capsule? ● A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or object part. ● It nests a new layer inside a layer. ● Instead of making a layer deeper in height, it makes a layer deeper in a structure.
  17. 17. More about Capsules ● Capsules are like cortical columns in human brains. ● Capsules are supposed to produce equivariant features, like a 3D graphic model: given the model with just a simple transformation we can derive all its poses
  18. 18. Dynamic Routing in Capsule NN Research paper published by Hinton - https://arxiv.org/abs/1710.09829v1
  19. 19. The cost of this new architecture? ● The data flow is more complicated ● No idea how stable it will be for attacking difficult learning problems. ● That makes it harder to calculate gradients, and the model may suffer more from vanishing gradients. ● Scalability?
  20. 20. Conclusion ● ConvNets is proven to solve many real world problems but it has its own drawbacks. ● Capsule Nets are a promising development to ConvNets. ● It is too early to predict the success of Capsule Nets because it is yet to be implemented on datasets other than MNIST dataset.
  21. 21. Thank You! ● Email - vaibhaw.vipul@gmail.com ● Github - https://github.com/vaibhawvipul

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