  In this paper, we introduce a convolutional network which we call MultiPodNet
consisting of a combination of two or more convolutional networks which process
the input image in parallel to achieve the same goal. Output feature maps of
parallel convolutional networks are fused at the fully connected layer of the
network. We experimentally observed that three parallel pod networks
(TripodNet) produce the best results in commonly used object recognition
datasets. Baseline pod networks can be of any type. In this paper, we use
ResNets as baseline networks and their inputs are augmented image patches. The
number of parameters of the TripodNet is about three times that of a single
ResNet. We train the TripodNet using the standard backpropagation type
algorithms. In each individual ResNet, parameters are initialized with
different random numbers during training. The TripodNet achieved
state-of-the-art performance on CIFAR-10 and ImageNet datasets. For example, it
improved the accuracy of a single ResNet from 91.66% to 92.47% under the same
training process on the CIFAR-10 dataset.
