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Toward Compact ConvNets via Structure-Sparsity Regularized Filter Pruning

https://doi.org/10.1109/tnnls.2019.2906563

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Cloud-Edge-End Cooperative Detection of Wind Turbine Blade Surface Damage Based on Lightweight Deep Learning Network

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A data-aware dictionary-learning based technique for the acceleration of deep convolutional networks

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Slimmer CNNs Through Feature Approximation and Kernel Size Reduction

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Improving Neural Network Architecture Compression by Multi-Grain Pruning

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Knowledge Distillation by Multiple Student Instance Interaction

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A Sensitivity-based Pruning Method for Convolutional Neural Networks

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MO-SAE:Multi-Objective Stacked Autoencoders Optimization for Edge Anomaly Detection

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Class-Separation Preserving Pruning for Deep Neural Networks

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Edge-Side Fine-Grained Sparse CNN Accelerator With Efficient Dynamic Pruning Scheme

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SASL: Saliency-Adaptive Sparsity Learning for Neural Network Acceleration

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An Automatically Layer-Wise Searching Strategy for Channel Pruning Based on Task-Driven Sparsity Optimization

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Highlight Every Step: Knowledge Distillation via Collaborative Teaching

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Inner-Imaging Networks: Put Lenses Into Convolutional Structure

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Sensor-based gait recognition using ensemble network unified with independent subnetworks

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Objective Extraction for Simplifying Many-Objective Solution Sets

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Sparsity-Aware Global Channel Pruning for Infrared Small-Target Detection Networks

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Bridging Artificial Intelligence and Devices: Power Reduction Method of Non-Volatile Devices With Error-Resilient Deep Neural Networks

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A Collaborative Compression Scheme for Fast Activity Recognition on Mobile Devices via Global Compression Ratio Decision

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Pruning 3D Filters For Accelerating 3D ConvNets

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Progressive Local Filter Pruning for Image Retrieval Acceleration

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EDP: An Efficient Decomposition and Pruning Scheme for Convolutional Neural Network Compression

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2020 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 31

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Filter Sketch for Network Pruning

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Network Pruning Using Adaptive Exemplar Filters

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Data-Independent Structured Pruning of Neural Networks via Coresets

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Recursion Newton-Like Algorithm for l 2,0-ReLU Deep Neural Networks

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Automatic Sparse Connectivity Learning for Neural Networks

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Filter Pruning by Switching to Neighboring CNNs With Good Attributes

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SOKS: Automatic Searching of the Optimal Kernel Shapes for Stripe-Wise Network Pruning

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Unsupervised Continual Learning in Streaming Environments

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Compression of Convolutional Neural Networks With Divergent Representation of Filters

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Power Law in Deep Neural Networks: Sparse Network Generation and Continual Learning With Preferential Attachment

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Hierarchical Threshold Pruning Based on Uniform Response Criterion

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Adaptive Filter Pruning via Sensitivity Feedback

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On-Device Deep Learning: Survey on Techniques Improving Energy Efficiency of DNNs

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Discrimination-aware Network Pruning for Deep Model Compression

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Compact Neural Network via Stacking Hybrid Units

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Structured Pruning for Deep Convolutional Neural Networks: A Survey

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A Survey on Deep Neural Network Pruning: Taxonomy, Comparison, Analysis, and Recommendations

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STPP: Efficient and Progressive Structured Pruning via Enhanced Sparsification Paradigm

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Advancements in Accelerating Deep Neural Network Inference on AIoT Devices: A Survey

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FinSAA: Sparse-Aware CNN Accelerator With Fast Irregular Index Decoding and Adaptive Dense-Sparse Convolution Architecture

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Adaptive Model Pruning for Communication and Computation Efficient Wireless Federated Learning

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Accelerating 3D scene analysis for autonomous driving on embedded AI computing platforms

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Sensitivity-Informed Provable Pruning of Neural Networks

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Deep Neural Networks Pruning via the Structured Perspective Regularization

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Research of Automobile Fault Lamp Recognition Algorithm Based on Mobile Platform

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Deep Learning on Computational-Resource-Limited Platforms: A Survey

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Compression towards the minimal performance-guaranteed neural network

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DPFPS: Dynamic and Progressive Filter Pruning for Compressing Convolutional Neural Networks from Scratch

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Dynamic Network Pruning Based on Expectation Scaling Factor

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Deep Clustering and Deep Network Compression

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Convolutional Neural Network Pruning: A Survey

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A Multi-Scale Automatic Progressive Pruning Algorithm Based on Deep Neural Network

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Beyond Network Pruning: a Joint Search-and-Training Approach

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A novel MapReduce-based deep convolutional neural network algorithm

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Common visual pattern discovery and analysis

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CRISP-ML(QS): Embedding Sustainability in AI-ML Development Lifecycle

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Cotton disease identification method based on pruning

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Compression of Deep Convolutional Neural Network Using Additional Importance-Weight-Based Filter Pruning Approach

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Filter Pruning with Convolutional Approximation Small Model Framework

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Coarse-Grained Pruning of Neural Network Models Based on Blocky Sparse Structure

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Feature Map Analysis-Based Dynamic CNN Pruning and the Acceleration on FPGAs

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3DAGNet: 3D Deep Attention and Global Search Network for Pulmonary Nodule Detection

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Optimizing Convolutional Neural Network Architectures

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Neural Network Compression via Low Frequency Preference

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Building a Compact Convolutional Neural Network for Embedded Intelligent Sensor Systems Using Group Sparsity and Knowledge Distillation

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IoTNet: An Efficient and Accurate Convolutional Neural Network for IoT Devices

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Efficient Layer Optimizations for Deep Neural Networks

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An Effective Backward Filter Pruning Algorithm Using K1,n Bipartite Graph-Based Clustering and the Decreasing Pruning Rate Approach

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An Effective Backward Filter Pruning Algorithm Using K1,n Bipartite Graph-Based Clustering and the Decreasing Pruning Rate Approach

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Dependency Aware Filter Pruning

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Novel Adaptive Binary Search Strategy-First Hybrid Pyramid- and Clustering-Based CNN Filter Pruning Method without Parameters Setting

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Neural Network Compression Via Sparse Optimization

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Parametric Rectified Power Sigmoid Units: Learning Nonlinear Neural Transfer Analytical Forms

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Structured Sparsity Inducing Adaptive Optimizers for Deep Learning

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Differentiable Neural Architecture Learning for Efficient Neural Network Design

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Similarity Transfer for Knowledge Distillation

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Only Train Once: A One-Shot Neural Network Training And Pruning Framework

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