Papers Neural Network Compression
“Neural Network Compression” 태그가 달린 논문 216편 · 필터 해제
Neural network compression via learnable wavelet transforms
Wavelets are well known for data compression, yet have rarely been applied to the compression of neural networks. This paper shows how the fast wavelet transform can be used to compress linear layers in neural networks. …
Data CompressionNeural Network CompressionTeacher-Class Network: A Neural Network Compression Mechanism
To reduce the overwhelming size of Deep Neural Networks (DNN) teacher-student methodology tries to transfer knowledge from a complex teacher network to a simple student network. We instead propose a novel method called t…
image-classificationImage ClassificationNeural Network CompressionSentiment Analysis+1DP-Net: Dynamic Programming Guided Deep Neural Network Compression
In this work, we propose an effective scheme (called DP-Net) for compressing the deep neural networks (DNNs). It includes a novel dynamic programming (DP) based algorithm to obtain the optimal solution of weight quantiza…
ClusteringNeural Network CompressionQuantizationMINT: Deep Network Compression via Mutual Information-based Neuron Trimming
Most approaches to deep neural network compression via pruning either evaluate a filter's importance using its weights or optimize an alternative objective function with sparsity constraints. While these methods offer a …
Neural Network CompressionNeural Network Compression Framework for fast model inference
In this work we present a new framework for neural networks compression with fine-tuning, which we called Neural Network Compression Framework (NNCF). It leverages recent advances of various network compression methods a…
BinarizationCPUGPUmodel+2The continuous categorical: a novel simplex-valued exponential family
Simplex-valued data appear throughout statistics and machine learning, for example in the context of transfer learning and compression of deep networks. Existing models for this class of data rely on the Dirichlet distri…
Neural Network CompressionTransfer LearningREST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild
In recent years, significant attention has been devoted towards integrating deep learning technologies in the healthcare domain. However, to safely and practically deploy deep learning models for home health monitoring, …
EEGElectroencephalogram (EEG)Neural Network CompressionSleep Stage Detection+1Quantisation and Pruning for Neural Network Compression and Regularisation
Deep neural networks are typically too computationally expensive to run in real-time on consumer-grade hardware and low-powered devices. In this paper, we investigate reducing the computational and memory requirements of…
Network PruningNeural Network CompressionZeroQ: A Novel Zero Shot Quantization Framework
Quantization is a promising approach for reducing the inference time and memory footprint of neural networks. However, most existing quantization methods require access to the original training dataset for retraining dur…
Data Free QuantizationModel CompressionNeural Network CompressionQuantizationTaxonomy and Evaluation of Structured Compression of Convolutional Neural Networks
The success of deep neural networks in many real-world applications is leading to new challenges in building more efficient architectures. One effective way of making networks more efficient is neural network compression…
Neural Network CompressionTOCO: A Framework for Compressing Neural Network Models Based on Tolerance Analysis
Neural network compression methods have enabled deploying large models on emerging edge devices with little cost, by adapting already-trained models to the constraints of these devices. The rapid development of AI-capabl…
Active LearningNeural Network CompressionCompressing 3DCNNs Based on Tensor Train Decomposition
Three dimensional convolutional neural networks (3DCNNs) have been applied in many tasks, e.g., video and 3D point cloud recognition. However, due to the higher dimension of convolutional kernels, the space complexity of…
Hand Gesture RecognitionHand-Gesture RecognitionNeural Network CompressionQuantization+1A Programmable Approach to Neural Network Compression
Deep neural networks (DNNs) frequently contain far more weights, represented at a higher precision, than are required for the specific task which they are trained to perform. Consequently, they can often be compressed us…
Bayesian OptimizationImage ClassificationLanguage ModellingModel Compression+2Distilling Pixel-Wise Feature Similarities for Semantic Segmentation
Among the neural network compression techniques, knowledge distillation is an effective one which forces a simpler student network to mimic the output of a larger teacher network. However, most of such model distillation…
Knowledge DistillationNeural Network CompressionSemantic SegmentationA Comparative Study of Neural Network Compression
There has recently been an increasing desire to evaluate neural networks locally on computationally-limited devices in order to exploit their recent effectiveness for several applications; such effectiveness has neverthe…
L2 RegularizationNeural Network CompressionAutomatic Neural Network Compression by Sparsity-Quantization Joint Learning: A Constrained Optimization-based Approach
Deep Neural Networks (DNNs) are applied in a wide range of usecases. There is an increased demand for deploying DNNs on devices that do not have abundant resources such as memory and computation units. Recently, network …
Neural Network CompressionQuantizationDeep Neural Network Compression for Image Classification and Object Detection
Neural networks have been notorious for being computationally expensive. This is mainly because neural networks are often over-parametrized and most likely have redundant nodes or layers as they are getting deeper and wi…
ClassificationClusteringGeneral Classificationimage-classification+5A Bayesian Optimization Framework for Neural Network Compression
Neural network compression is an important step for deploying neural networks where speed is of high importance, or on devices with limited memory. It is necessary to tune compression parameters in order to achieve the d…
Bayesian OptimizationKnowledge DistillationNeural Network CompressionDistilled Split Deep Neural Networks for Edge-Assisted Real-Time Systems
Offloading the execution of complex Deep Neural Networks (DNNs) models to compute-capable devices at the network edge, that is, edge servers, can significantly reduce capture-to-output delay. However, the communication l…
Edge-computingImage ClassificationKnowledge DistillationModel Compression+1XNOR-Net++: Improved Binary Neural Networks
This paper proposes an improved training algorithm for binary neural networks in which both weights and activations are binary numbers. A key but fairly overlooked feature of the current state-of-the-art method of XNOR-N…
BinarizationClassification with Binary Neural NetworkImage ClassificationNeural Network Compression+1