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It is well observed that in deep learning and computer vision literature,visual data are always represented in a manually designed coding scheme (eg.,RGB images are represented as integers ranging from 0 to 255 for each channel)when they are input to an end-to-end deep neural network (DNN) for any learningtask. We boldly question whether the manually designed inputs are good for DNNtraining for different tasks and study whether the input to a DNN can beoptimally learned end-to-end together with learning the weights of the DNN. Inthis paper, we propose the paradigm of {\em deep collective learning} whichaims to learn the weights of DNNs and the inputs to DNNs simultaneously forgiven tasks. We note that collective learning has been implicitly but widelyused in natural language processing while it has almost never been studied incomputer vision. Consequently, we propose the lookup vision networks(Lookup-VNets) as a solution to deep collective learning in computer vision.This is achieved by associating each color in each channel with a vector inlookup tables. As learning inputs in computer vision has almost never beenstudied in the existing literature, we explore several aspects of this questionthrough varieties of experiments on image classification tasks. Experimentalresults on four benchmark datasets, i.e., CIFAR-10, CIFAR-100, Tiny ImageNet,and ImageNet (ILSVRC2012) have shown several surprising characteristics ofLookup-VNets and have demonstrated the advantages and promise of Lookup-VNetsand deep collective learning.

Deep Collective Learning: Learning Optimal Inputs and Weights Jointly in Deep Neural Networks