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 401–425 of 1356 papers

TitleStatusHype
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
Efficient Point Cloud Classification via Offline Distillation Framework and Negative-Weight Self-Distillation Technique—0
Foundations of Large Language Model Compression -- Part 1: Weight QuantizationCode0
Edge AI: Evaluation of Model Compression Techniques for Convolutional Neural Networks—0
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
A Web-Based Solution for Federated Learning with LLM-Based Automation—0
A Survey on Drowsiness Detection -- Modern Applications and Methods—0
Fine-Tuning and Deploying Large Language Models Over Edges: Issues and Approaches—0
NeR-VCP: A Video Content Protection Method Based on Implicit Neural Representation—0
MoDeGPT: Modular Decomposition for Large Language Model Compression—0
RepControlNet: ControlNet Reparameterization—0
Computer Vision Model Compression Techniques for Embedded Systems: A SurveyCode0
An Effective Information Theoretic Framework for Channel Pruning—0
Infra-YOLO: Efficient Neural Network Structure with Model Compression for Real-Time Infrared Small Object Detection—0
AdapMTL: Adaptive Pruning Framework for Multitask Learning Model—0
DopQ-ViT: Towards Distribution-Friendly and Outlier-Aware Post-Training Quantization for Vision Transformers—0
Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments—0
Comb, Prune, Distill: Towards Unified Pruning for Vision Model CompressionCode0
An Efficient Real-Time Object Detection Framework on Resource-Constricted Hardware Devices via Software and Hardware Co-design—0
Artificial Neural Networks for Photonic Applications: From Algorithms to Implementation—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