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 526–550 of 1356 papers

TitleStatusHype
From Large to Super-Tiny: End-to-End Optimization for Cost-Efficient LLMs—0
Cross Domain Model Compression by Structurally Weight Sharing—0
Inferring ECG from PPG for Continuous Cardiac Monitoring Using Lightweight Neural Network—0
From Word Vectors to Multimodal Embeddings: Techniques, Applications, and Future Directions For Large Language Models—0
Cross-Channel Intragroup Sparsity Neural Network—0
Croesus: Multi-Stage Processing and Transactions for Video-Analytics in Edge-Cloud Systems—0
Attention Sinks and Outlier Features: A 'Catch, Tag, and Release' Mechanism for Embeddings—0
Creating Lightweight Object Detectors with Model Compression for Deployment on Edge Devices—0
CPTQuant -- A Novel Mixed Precision Post-Training Quantization Techniques for Large Language Models—0
ALF: Autoencoder-based Low-rank Filter-sharing for Efficient Convolutional Neural Networks—0
AACP: Model Compression by Accurate and Automatic Channel Pruning—0
Frustratingly Easy Model Ensemble for Abstractive Summarization—0
FSCNN: A Fast Sparse Convolution Neural Network Inference System—0
Atrial Fibrillation Detection Using Weight-Pruned, Log-Quantised Convolutional Neural Networks—0
“Learning-Compression” Algorithms for Neural Net Pruning—0
Integrating Fairness and Model Pruning Through Bi-level Optimization—0
CoSurfGS:Collaborative 3D Surface Gaussian Splatting with Distributed Learning for Large Scene Reconstruction—0
Atomic Compression Networks—0
Fragile Mastery: Are Domain-Specific Trade-Offs Undermining On-Device Language Models?—0
Atleus: Accelerating Transformers on the Edge Enabled by 3D Heterogeneous Manycore Architectures—0
Cosine Similarity Knowledge Distillation for Individual Class Information Transfer—0
Spike-and-slab shrinkage priors for structurally sparse Bayesian neural networks—0
CORSD: Class-Oriented Relational Self Distillation—0
A Theoretical Understanding of Neural Network Compression from Sparse Linear Approximation—0
A Half-Space Stochastic Projected Gradient Method for Group Sparsity Regularization—0
Show:102550
← PrevPage 22 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