wide-residual-networks
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3.8% and 18.3% on CIFAR-10 and CIFAR-100
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wide-residual-networks issues
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do you need machines to test?
Could you please post your results here (http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html)? It is nice to keep track of best-performing methods on standard datasets such as MNIST, CIFAR-10, CIFAR-100 and SVHN.
I'm trying to whiten CIFAR-10 following the instructions and using the mentioned scripts. I succeed got `train.npy` and `test.npy` out from `make_cifar10_gcn_whitened.py`. But I couldn't figure out how to convert...
HI, szagoruyko Thank you for the excellent code! I am developing a CNN for activity recognition. Our model will largely benefit with a pre-trained model. I am wondering if you...