Improved Residual Networks for Image and Video Recognition

Ionut Cosmin Duta, Li Liu, Fan Zhu, Ling Shao

Abstract:

Residual networks (ResNets) represent a powerful type of
convolutional neural network (CNN) architecture, widely adopted and
used in various tasks. In this work we propose an improved version of
ResNets. Our proposed improvements address all three main components of a ResNet: the flow of information through the network layers,
the residual building block, and the projection shortcut. We are able
to show consistent improvements in accuracy and learning convergence
over the baseline. For instance, on ImageNet dataset, using the ResNet
with 50 layers, for top-1 accuracy we can report a 1.19% improvement
over the baseline in one setting and around 2% boost in another. Importantly, these improvements are obtained without increasing the model complexity. Our proposed approach allows us to train extremely deep
networks, while the baseline shows severe optimization issues. We report results on three tasks over six datasets: image classification (ImageNet, CIFAR-10 and CIFAR-100), object detection (COCO) and video
action recognition (Kinetics-400 and Something-Something-v2). In the
deep learning era, we establish a new milestone for the depth of a CNN.
We successfully train a 404-layer deep CNN on the ImageNet dataset
and a 3002-layer network on CIFAR-10 and CIFAR-100, while the baseline is not able to converge at such extreme depths. Code is available at:
https://github.com/iduta/iresnet