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In the white-box setting, our defense works by obfuscating the parameters of the random projection.
We design an efficient FPGA-based accelerator for our novel BNN model that supports the fractional activations.
Furthermore, to improve the segmentation quality for different density regions, we present a differentiable Binarization Module (BM) to output structured instance maps.
In massive multiple-input multiple-output (MIMO) system, channel state information (CSI) is essential for the base station to achieve high performance gain.
In the first stage, four color-independent adversarial networks are trained to extract color foreground information from an input image for document image enhancement.
Documents often exhibit various forms of degradation, which make it hard to be read and substantially deteriorate the performance of an OCR system.
To alleviate the resource constraint for real-time point cloud applications that run on edge devices, in this paper we present BiPointNet, the first model binarization approach for efficient deep learning on point clouds.
In this paper, for the first time, we explore the influence of angular bias on the quantization error and then introduce a Rotated Binary Neural Network (RBNN), which considers the angle alignment between the full-precision weight vector and its binarized version.