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
When Compression Meets Model Compression: Memory-Efficient Double Compression for Large Language Models—0
XAI-BayesHAR: A novel Framework for Human Activity Recognition with Integrated Uncertainty and Shapely Values—0
YANMTT: Yet Another Neural Machine Translation Toolkit—0
You Only Prune Once: Designing Calibration-Free Model Compression With Policy Learning—0
Individual Content and Motion Dynamics Preserved Pruning for Video Diffusion Models—0
InfantCryNet: A Data-driven Framework for Intelligent Analysis of Infant Cries—0
Inference Optimization of Foundation Models on AI Accelerators—0
Information-Theoretic GAN Compression with Variational Energy-based Model—0
Infra-YOLO: Efficient Neural Network Structure with Model Compression for Real-Time Infrared Small Object Detection—0
InhibiDistilbert: Knowledge Distillation for a ReLU and Addition-based Transformer—0
INSIGHT: A Survey of In-Network Systems for Intelligent, High-Efficiency AI and Topology Optimization—0
Instance-Aware Group Quantization for Vision Transformers—0
Integral Pruning on Activations and Weights for Efficient Neural Networks—0
PublicCheck: Public Integrity Verification for Services of Run-time Deep Models—0
Interpreting Deep Classifier by Visual Distillation of Dark Knowledge—0
Redundancy and Concept Analysis for Code-trained Language Models—0
Intrinsically Sparse Long Short-Term Memory Networks—0
Investigation of Practical Aspects of Single Channel Speech Separation for ASR—0
Is Quantum Optimization Ready? An Effort Towards Neural Network Compression using Adiabatic Quantum Computing—0
IteRABRe: Iterative Recovery-Aided Block Reduction—0
Iterative Compression of End-to-End ASR Model using AutoML—0
It's always personal: Using Early Exits for Efficient On-Device CNN Personalisation—0
Joint Neural Architecture Search and Quantization—0
Joint Regularization on Activations and Weights for Efficient Neural Network Pruning—0
KDH-MLTC: Knowledge Distillation for Healthcare Multi-Label Text Classification—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