Quantization reduces model size and speeds up inference time by reducing the number of bits required to represent weights or activations. In NNI, both post-training quantization algorithms and ...
Recent advances in image data proccesing through deep learning allow for new optimization and performance-enhancement schemes for radiation detectors and imaging hardware. This enables radiation ...
Abstract: The quantization step is a crucial parameter in JPEG compression, that can reveal the compression history of a JPEG image. Estimating the quantization steps for single compressed and ...
ABSTRACT: A face-mask object detection model incorporating hybrid dilation convolutional network termed ResNet Hybrid-dilation-convolution Face-mask-detector (RHF) is proposed in this paper.
Nonvolatile memory (NVM)-based convolutional neural networks (NvCNNs) have received widespread attention as a promising solution for hardware edge intelligence. However, there still exist many ...
Digital images from today's premium digital cameras have a higher dynamic range than what humans can perceive. Maximumly keeping the image information captured by sensors while reducing the file size ...
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