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 351–400 of 1356 papers

TitleStatusHype
DeepTwist: Learning Model Compression via Occasional Weight Distortion—0
DeGAN : Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier—0
Delving Deep into Semantic Relation Distillation—0
Densely Distilling Cumulative Knowledge for Continual Learning—0
Deep Model Compression Via Two-Stage Deep Reinforcement Learning—0
Deep Model Compression: Distilling Knowledge from Noisy Teachers—0
Deep Model Compression based on the Training History—0
Deploying Foundation Model Powered Agent Services: A Survey—0
A Web-Based Solution for Federated Learning with LLM-Based Automation—0
Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing—0
Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey—0
Developing Far-Field Speaker System Via Teacher-Student Learning—0
Differentiable Architecture Compression—0
Differentiable Feature Aggregation Search for Knowledge Distillation—0
Differentiable Mask for Pruning Convolutional and Recurrent Networks—0
Edge-First Language Model Inference: Models, Metrics, and Tradeoffs—0
Differentiable Network Pruning for Microcontrollers—0
Differentiable Sparsification for Deep Neural Networks—0
AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates—0
Differentiable Sparsification for Deep Neural Networks—0
Deep learning model compression using network sensitivity and gradients—0
Differential Privacy Meets Federated Learning under Communication Constraints—0
EDCompress: Energy-Aware Model Compression for Dataflows—0
Dimensionality Reduced Training by Pruning and Freezing Parts of a Deep Neural Network, a Survey—0
DiPaCo: Distributed Path Composition—0
DipSVD: Dual-importance Protected SVD for Efficient LLM Compression—0
AMD: Adaptive Masked Distillation for Object Detection—0
Discrete Model Compression With Resource Constraint for Deep Neural Networks—0
DEEPEYE: A Compact and Accurate Video Comprehension at Terminal Devices Compressed with Quantization and Tensorization—0
Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks—0
Edge AI: Evaluation of Model Compression Techniques for Convolutional Neural Networks—0
Automatic Mixed-Precision Quantization Search of BERT—0
An Effective Information Theoretic Framework for Channel Pruning—0
Deep Compression of Neural Networks for Fault Detection on Tennessee Eastman Chemical Processes—0
AdaKD: Dynamic Knowledge Distillation of ASR models using Adaptive Loss Weighting—0
Distilling Inductive Bias: Knowledge Distillation Beyond Model Compression—0
Automatic Mapping of the Best-Suited DNN Pruning Schemes for Real-Time Mobile Acceleration—0
BioNetExplorer: Architecture-Space Exploration of Bio-Signal Processing Deep Neural Networks for Wearables—0
Deep Collective Knowledge Distillation—0
MobiSR: Efficient On-Device Super-Resolution through Heterogeneous Mobile Processors—0
Edge-AI for Agriculture: Lightweight Vision Models for Disease Detection in Resource-Limited Settings—0
Edge-MultiAI: Multi-Tenancy of Latency-Sensitive Deep Learning Applications on Edge—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
Decoupling Weight Regularization from Batch Size for Model Compression—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