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 676–700 of 1356 papers

TitleStatusHype
Radio: Rate-Distortion Optimization for Large Language Model Compression—0
Random Conditioning for Diffusion Model Compression with Distillation—0
Random Conditioning with Distillation for Data-Efficient Diffusion Model Compression—0
Random Offset Block Embedding Array (ROBE) for CriteoTB Benchmark MLPerf DLRM Model : 1000 Compression and 3.1 Faster Inference—0
RAND: Robustness Aware Norm Decay For Quantized Seq2seq Models—0
Rank-Based Filter Pruning for Real-Time UAV Tracking—0
RankDVQA-mini: Knowledge Distillation-Driven Deep Video Quality Assessment—0
Rapid Deployment of DNNs for Edge Computing via Structured Pruning at Initialization—0
Rate Distortion For Model Compression: From Theory To Practice—0
Experimental implementation of a neural network optical channel equalizer in restricted hardware using pruning and quantization—0
Real time backbone for semantic segmentation—0
Membership Privacy for Machine Learning Models Through Knowledge Transfer—0
Rectifying the Data Bias in Knowledge Distillation—0
Recurrent Convolution for Compact and Cost-Adjustable Neural Networks: An Empirical Study—0
Recurrent Convolutions: A Model Compression Point of View—0
Reducing Computational Complexity of Neural Networks in Optical Channel Equalization: From Concepts to Implementation—0
Reinforced Multi-Teacher Selection for Knowledge Distillation—0
Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence—0
Re-Parameterization of Lightweight Transformer for On-Device Speech Emotion Recognition—0
RepControlNet: ControlNet Reparameterization—0
Representation Transfer by Optimal Transport—0
REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs—0
Residual Knowledge Distillation—0
ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification—0
Resource Allocation for Compression-aided Federated Learning with High Distortion Rate—0
Show:102550
← PrevPage 28 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