  We present new Recurrent Neural Network (RNN) cells for image classification
using a Neural Architecture Search (NAS) approach called DARTS. We are
interested in the ReNet architecture, which is a RNN based approach presented
as an alternative for convolutional and pooling steps. ReNet can be defined
using any standard RNN cells, such as LSTM and GRU. One limitation is that
standard RNN cells were designed for one dimensional sequential data and not
for two dimensions like it is the case for image classification. We overcome
this limitation by using DARTS to find new cell designs. We compare our results
with ReNet that uses GRU and LSTM cells. Our found cells outperform the
standard RNN cells on CIFAR-10 and SVHN. The improvements on SVHN indicate
generalizability, as we derived the RNN cell designs from CIFAR-10 without
performing a new cell search for SVHN.
