PresB-Net: parametric binarized neural network with learnable activations and shuffled grouped convolution

Summary: AresB-Net introduces a novel network model for accurately binarized residual convolutional neural networks, reducing errors from binarization through feature reuse and decreasing computational costs and storage requirements. Despite low hardware costs, the model achieves remarkable classification accuracies on CIFAR and ImageNet datasets.