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 376–400 of 1356 papers

TitleStatusHype
DipSVD: Dual-importance Protected SVD for Efficient LLM Compression—0
DEEPEYE: A Compact and Accurate Video Comprehension at Terminal Devices Compressed with Quantization and Tensorization—0
Discrete Model Compression With Resource Constraint for Deep Neural Networks—0
Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks—0
ECoFLaP: Efficient Coarse-to-Fine Layer-Wise Pruning for Vision-Language Models—0
Automatic Mixed-Precision Quantization Search of BERT—0
Deep Compression of Neural Networks for Fault Detection on Tennessee Eastman Chemical Processes—0
Automatic Mapping of the Best-Suited DNN Pruning Schemes for Real-Time Mobile Acceleration—0
Deep Collective Knowledge Distillation—0
An Effective Information Theoretic Framework for Channel Pruning—0
Distilling Inductive Bias: Knowledge Distillation Beyond Model Compression—0
MobiSR: Efficient On-Device Super-Resolution through Heterogeneous Mobile Processors—0
BioNetExplorer: Architecture-Space Exploration of Bio-Signal Processing Deep Neural Networks for Wearables—0
EDCompress: Energy-Aware Model Compression for Dataflows—0
Edge Deep Learning for Neural Implants—0
Decoupling Weight Regularization from Batch Size for Model Compression—0
Distilling Spikes: Knowledge Distillation in Spiking Neural Networks—0
Distilling with Performance Enhanced Students—0
Distributed Low Precision Training Without Mixed Precision—0
Divergent Token Metrics: Measuring degradation to prune away LLM components -- and optimize quantization—0
DKM: Differentiable K-Means Clustering Layer for Neural Network Compression—0
DLIP: Distilling Language-Image Pre-training—0
DMT: Comprehensive Distillation with Multiple Self-supervised Teachers—0
DNA data storage, sequencing data-carrying DNA—0
Debiased Distillation by Transplanting the Last Layer—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