Before revision:  The best performing model was the generative Analysis-by-Synthesis model (based on variational autoencoders) . However, a simpler generative model (based on Gaussian-kernel-density estimation) also performed better than each of the discriminative models . None of the candidate models fully explained the human responses.
After revision:  The best performing model was the generative Analysis-by-Synthesis model (based on variational autoencoders) for MNIST and a hybrid discriminative-generative Joint Energy Model for CIFAR-10. These DNNs, which model the distribution of images, performed better than each of the discriminative models . None of the candidate models fully explained the human responses.