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I haven't read the research papers yet, but as someone new to machine learning and image recognition... > Hinton’s capsule networks matched the accuracy of the

• yorwba 9y ago
  Yes, their first paper is on MNIST. That they get high accuracy isn't earth-shattering, but since they are doing something very different from other approaches, it's still noteworthy. The real benefit is in the generalization performance: We then tested this network on the affNIST 4 data set, in which each example is an MNIST digit with a random small affine transformation. Our models were never trained with affine transformations other than translation and any natural transformation seen in the standard MNIST. An under-trained CapsNet with early stopping which achieved 99.23% accuracy on the expanded MNIST test set achieved 79% accuracy on the affnist test set. A traditional convolutional model with a similar number of parameters which achieved similar accuracy (99.22%) on the expanded mnist test set but only achieved 66% on the affnist test set.
  • amelius 9y ago
    Yes that seems like a good first test for generalization. Did they publish these images somewhere?
• c3534l 9y ago
  In Machine Learning terms, current neural network techniques are a local minima. Capsule networks might not perform as well now, but may offer a way forward which will outperform them in the long term as they're developed and refined. Being able to match MNIST results, rather than do worse, is a good reason to work on them.