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Deep neural networks (DNN) such as Multi-Layer Perception (MLP) andConvolutional Neural Networks (CNN) represent one of the most established deeplearning algorithms. Given the tremendous effects of the number of hiddenlayers on network architecture and performance, it is very important to choosethe number of hidden layers but still a serious challenge. More importantly,the current network architectures can only process the information from thelast layer of the feature extractor, which greatly limited us to furtherimprove its performance. Here we presented a layer-stress deep learningframework (x-NN) which implemented automatic and wise depth decision on shallowor deep feature map in a deep network through firstly designing enough numberof layers and then trading off them by Multi-Head Attention Block. The x-NN canmake use of features from various depth layers through attention allocation andthen help to make final decision as well. As a result, x-NN showed outstandingprediction ability in the Alzheimer's Disease Classification TechniqueChallenge PRCV 2021, in which it won the top laurel and outperformed all otherAI models. Moreover, the performance of x-NN was verified by one more ADneuroimaging dataset and other AI tasks.

A layer-stress learning framework universally augments deep neural network tasks