From Springer Nature via Jisc Publications RouterHistory: received 2020-10-12, accepted 2021-06-03, registration 2021-06-25, pub-electronic 2021-07-11, online 2021-07-11, pub-print 2021-11Publication status: PublishedFunder: Toyota Motor Europe; doi: http://dx.doi.org/10.13039/501100010577Funder: Faculty of Science and Engineering, University of Manchester (GB); Grant(s): 1Abstract: We introduce a new architecture called ChoiceNet where each layer of the network is highly connected with skip connections and channelwise concatenations. This enables the network to alleviate the problem of vanishing gradients, reduces the number of parameters without sacrificing performance and encourages feature reuse. We evaluate our proposed architecture on three independent tasks: classification, segmentation and facial landmark localisation. For this, we use benchmark datasets such as ImageNet, CIFAR-10, CIFAR-100, SVHN CamVid and 300W
