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 201–225 of 1356 papers

TitleStatusHype
Compressing Recurrent Neural Networks for FPGA-accelerated Implementation in Fluorescence Lifetime Imaging—0
Trainable pruned ternary quantization for medical signal classification modelsCode0
Aggressive Post-Training Compression on Extremely Large Language Models—0
InfantCryNet: A Data-driven Framework for Intelligent Analysis of Infant Cries—0
Value-Based Deep Multi-Agent Reinforcement Learning with Dynamic Sparse Training—0
General Compression Framework for Efficient Transformer Object Tracking—0
MaskLLM: Learnable Semi-Structured Sparsity for Large Language ModelsCode2
Search for Efficient Large Language ModelsCode1
Enhancing Knowledge Distillation of Large Language Models through Efficient Multi-Modal Distribution AlignmentCode0
Applications of Knowledge Distillation in Remote Sensing: A Survey—0
ELSA: Exploiting Layer-wise N:M Sparsity for Vision Transformer AccelerationCode0
Privacy-Preserving SAM Quantization for Efficient Edge Intelligence in Healthcare—0
NVRC: Neural Video Representation Compression—0
Application Specific Compression of Deep Learning ModelsCode0
Ultron: Enabling Temporal Geometry Compression of 3D Mesh Sequences using Temporal Correspondence and Mesh DeformationCode0
LoCa: Logit Calibration for Knowledge Distillation—0
Designing Large Foundation Models for Efficient Training and Inference: A SurveyCode1
Foundations of Large Language Model Compression -- Part 1: Weight QuantizationCode0
Efficient Point Cloud Classification via Offline Distillation Framework and Negative-Weight Self-Distillation Technique—0
Edge AI: Evaluation of Model Compression Techniques for Convolutional Neural Networks—0
Hyper-Compression: Model Compression via HyperfunctionCode1
MedDet: Generative Adversarial Distillation for Efficient Cervical Disc Herniation DetectionCode0
Convolutional Neural Network Compression Based on Low-Rank Decomposition—0
Variational autoencoder-based neural network model compression—0
MPruner: Optimizing Neural Network Size with CKA-Based Mutual Information Pruning—0
Show:102550
← PrevPage 9 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