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 201–250 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
Foundations of Large Language Model Compression -- Part 1: Weight QuantizationCode0
Designing Large Foundation Models for Efficient Training and Inference: A SurveyCode1
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
Localize-and-Stitch: Efficient Model Merging via Sparse Task ArithmeticCode1
A Web-Based Solution for Federated Learning with LLM-Based Automation—0
A Survey on Drowsiness Detection -- Modern Applications and Methods—0
Pruning By Explaining Revisited: Optimizing Attribution Methods to Prune CNNs and TransformersCode1
NeR-VCP: A Video Content Protection Method Based on Implicit Neural Representation—0
Fine-Tuning and Deploying Large Language Models Over Edges: Issues and Approaches—0
MoDeGPT: Modular Decomposition for Large Language Model Compression—0
RepControlNet: ControlNet Reparameterization—0
ABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language ModelsCode3
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
Knowledge Distillation with Refined LogitsCode1
Compact 3D Gaussian Splatting for Static and Dynamic Radiance FieldsCode3
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
Artificial Neural Networks for Photonic Applications: From Algorithms to Implementation—0
An Efficient Real-Time Object Detection Framework on Resource-Constricted Hardware Devices via Software and Hardware Co-design—0
Tensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMs—0
NeuSemSlice: Towards Effective DNN Model Maintenance via Neuron-level Semantic Slicing—0
Generalizing Teacher Networks for Effective Knowledge Distillation Across Student ArchitecturesCode0
Comprehensive Study on Performance Evaluation and Optimization of Model Compression: Bridging Traditional Deep Learning and Large Language Models—0
LORTSAR: Low-Rank Transformer for Skeleton-based Action Recognition—0
Show:102550
← PrevPage 5 of 28Next →
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