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As I know, the current state of the art methods for training deep learning networks are variants of gradient descent or stochastic gradient descent.

What are the best known gradient-free training methods for deep learning (mostly in visual tasks context)?

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There are several different algorithms that can be used for gradient free neural network training. Some of these algorithms include particle swarm optimization, genetic algorithms, simulated annealing, and several others. Almost any optimization algorithm can be used to train a neural network. Here is an overview of some of the algorithms I listed:

  • Particle Swarm optimization - I would say that this is one of the better optimization algorithms to train neural networks other than back propagation. I am currently using it and have achieved quite good results.
  • Genetic Algorithms - I have tried to use genetic algorithms to train neural networks in the past and I was not able to get it to work. However, I was using deep neural networks with almost a million parameters and the performance was not that good.
  • Simulated annealing - simulated annealing is based off of metals cooling. I have seen simulated annealing work fairly well but maybe not as well as particle swarm optimization.
  • Derivatives of genetic algorithms - derivatives of genetic algorithms such as NEAT have been shown to work pretty well. I have not personally used them extensively but some of the things that people have used them for are pretty cool.
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