Search Results for author:
Found 1 papers, 0 papers with code
Learning Identity Mappings with Residual Gates
We evaluate our method on MNIST using fully-connected networks, showing empirical indications that our augmentation facilitates the optimization of deep models, and that it provides high tolerance to full layer removal: the model retains over 90% of its performance even after half of its layers have been randomly removed.
Ranked #108 on
Image Classification
on CIFAR-10
Cannot find the paper you are looking for? You can
Submit a new open access paper.
