I don’t expect my approach to be helpful on CIFAR 10 - there’s not enough similar classes. An earlier post in this thread however shows how to hierarchically group CIFAR 100, which I think should help. Another approach would be word2vec class similarities in imagenet (you can use the 64 pixel size version to make it faster).
I’d suggest using @radek’s CIFAR10 baseline - that’ll get you 94% accuracy in <15 mins. I’d be cautious of interpreting approaches which much poorer baseline; (I’ve found that papers that build on poor baselines often have conclusions that turn out not to be relevant in practice.)