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 876–900 of 1356 papers

TitleStatusHype
Kronecker Decomposition for GPT Compression—0
Differentiable Network Pruning for Microcontrollers—0
A Memory-Efficient Learning Framework for SymbolLevel Precoding with Quantized NN Weights—0
Rectifying the Data Bias in Knowledge Distillation—0
FedDQ: Communication-Efficient Federated Learning with Descending Quantization—0
KIMERA: Injecting Domain Knowledge into Vacant Transformer Heads—0
A Unified Knowledge Distillation Framework for Deep Directed Graphical Models—0
Sparse Unbalanced GAN Training with In-Time Over-Parameterization—0
HFSP: A Hardware-friendly Soft Pruning Framework for Vision Transformers—0
Model Compression via Symmetries of the Parameter Space—0
Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning—0
Robot Intent Recognition Method Based on State Grid Business Office—0
Prototypical Contrastive Predictive Coding—0
Bayesian Optimization with Clustering and Rollback for CNN Auto PruningCode0
Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications—0
Experimental implementation of a neural network optical channel equalizer in restricted hardware using pruning and quantization—0
A Note on Knowledge Distillation Loss Function for Object Classification—0
Multihop: Leveraging Complex Models to Learn Accurate Simple Models—0
KroneckerBERT: Learning Kronecker Decomposition for Pre-trained Language Models via Knowledge Distillation—0
Causal Explanation of Convolutional Neural NetworksCode0
BioNetExplorer: Architecture-Space Exploration of Bio-Signal Processing Deep Neural Networks for Wearables—0
GDP: Stabilized Neural Network Pruning via Gates with Differentiable Polarization—0
Full-Cycle Energy Consumption Benchmark for Low-Carbon Computer Vision—0
Lipschitz Continuity Guided Knowledge Distillation—0
DKM: Differentiable K-Means Clustering Layer for Neural Network Compression—0
Show:102550
← PrevPage 36 of 55Next →
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