SOTAVerified

Model Compression

Model Compression is an actively pursued area of research over the last few years with the goal of deploying state-of-the-art deep networks in low-power and resource limited devices without significant drop in accuracy. Parameter pruning, low-rank factorization and weight quantization are some of the proposed methods to compress the size of deep networks.

Source: KD-MRI: A knowledge distillation framework for image reconstruction and image restoration in MRI workflow

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1251–1275 of 1356 papers

TitleStatusHype
Leveraging Filter Correlations for Deep Model Compression—0
A Survey of Mobile Computing for the Visually Impaired—0
Joint Neural Architecture Search and Quantization—0
Graph-Adaptive Pruning for Efficient Inference of Convolutional Neural Networks—0
Stability Based Filter Pruning for Accelerating Deep CNNs—0
Three Dimensional Convolutional Neural Network Pruning with Regularization-Based Method—0
Private Model Compression via Knowledge Distillation—0
Sequence-Level Knowledge Distillation for Model Compression of Attention-based Sequence-to-Sequence Speech Recognition—0
FLOPs as a Direct Optimization Objective for Learning Sparse Neural Networks—0
A Unified Framework of DNN Weight Pruning and Weight Clustering/Quantization Using ADMM—0
JavaScript Convolutional Neural Networks for Keyword Spotting in the Browser: An Experimental AnalysisCode0
DeepTwist: Learning Model Compression via Occasional Weight Distortion—0
Discrimination-aware Channel Pruning for Deep Neural NetworksCode1
A Miniaturized Semantic Segmentation Method for Remote Sensing ImageCode0
Distilling with Performance Enhanced Students—0
To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inference—0
Compression of Deep Neural Networks by combining pruning and low rank decomposition—0
Block-wise Intermediate Representation Training for Model Compression—0
PocketFlow: An Automated Framework for Compressing and Accelerating Deep Neural NetworksCode0
Recurrent Convolutions: A Model Compression Point of View—0
Pruning at a Glance: A Structured Class-Blind Pruning Technique for Model Compression—0
Progressive Weight Pruning of Deep Neural Networks using ADMM—0
Dynamic Channel Pruning: Feature Boosting and SuppressionCode1
Rate Distortion For Model Compression: From Theory To Practice—0
Efficient Computation of Quantized Neural Networks by −1, +1 Encoding Decomposition—0
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All datasetsImageNetQNLI

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ADLIK-MO-ResNet50+W4A4Top-177.88—Unverified
2ADLIK-MO-ResNet50+W3A4Top-177.34—Unverified
3ResNet-18 + 4bit-1dim model compression using DKMTop-170.52—Unverified
4MobileNet-v1 + 4bit-1dim model compression using DKMTop-169.63—Unverified
5ResNet-18 + 2bit-1dim model compression using DKMTop-168.63—Unverified
6MobileNet-v1 + 2bit-1dim model compression using DKMTop-167.62—Unverified
7ResNet-18 + 4bit-4dim model compression using DKMTop-166.1—Unverified
8ResNet-18 + 2bit-2dim model compression using DKMTop-164.7—Unverified
9MobileNet-v1 + 4bit-4dim model compression using DKMTop-161.4—Unverified
10ResNet-18 + 1bit-1dim model compression using DKMTop-159.7—Unverified
#ModelMetricClaimedVerifiedStatus
1MobileBERT + 2bit-1dim model compression using DKMAccuracy82.13—Unverified
2MobileBERT + 1bit-1dim model compression using DKMAccuracy63.17—Unverified