[ICML2015]Deep Learning with Limited Numerical Precision 2 聚类量化:Deep Compression 聚类量化来源于韩松ICLR2016的论文Deep Compression。聚类量化是就是把权重和梯度相近的值使用K-means聚类,然后将同类的数统一替换为与之相近的浮点数。聚类后权重字典对应的value保存量化后的权重值,字典的key保存量化值的索引。
DNNoriginal gradation hologramsJPEG 2000This study proposes a dynamic-range compression for digital holograms generated from three-dimensional scenes using deep neural network (DNN). This method uses an error diffusion algorithm to binarize holograms with an 8-bit gradation; moreover, the DNN predicts...
To address this problem, modern solutions involve the use of compression techniques to reduce the memory footprint of deep learning models while saving the accuracy performance. The proposed work focuses on plant disease detection which represents one of the biggest challenges in smart agriculture; in...
[44] Raanan Fattal, Dani Lischinski, and Michael Werman.Gradient domain high dynamic range compression. InACM Siggraph Computer Graphics, 2002. [45] Ulrich Fecker, Marcus Barkowsky, and Andr´e Kaup.Histogram-based prefiltering for luminance and chromi-nance compensation of multiview video. I...
planner components play a critical role. This enhances cloud resource workloads and diversity performance while lowering costs. We present hybrid optimum and deep learning approach for dynamic scalable task scheduling (DSTS) in container cloud environment in this research. To expand containers virtual res...
A Deep Learning Approach to Data Compression 4.5 VAE, Bits-Back Coding Bits-back coding is a form of lossless compression that addresses the entropy overestimation of using latent variable models. Figure 1: Overview of lossless compression. First, the sender encodes data to a code with the small...
Performance evaluations of deepCoSeL with synthetic data were presented in71,72. In particular71, investigates a known trade-off in compressed seismic learning. This is related to the compression limit for which the estimation errors are acceptable47. The same tradeoff has been observed in simulation...
A communication-efficient distributed deep learning remote sensing image change detection framework A distributed deep learning change detection model training framework for network-constrained systems is proposed.Gradient compression approaches are intro... H Cheng,J Zheng,H Wu,... - 《International Journ...
In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving ou
Intel® Neural Compressoris one of the key AI software components in the Intel® oneAPI AI Analytics Toolkit. It is an open-source Python library that runs on Intel CPUs and GPUs. This toolkit delivers unified interfaces across multiple deep learning frameworks for popular network compressi...