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 1051–1075 of 1356 papers

TitleStatusHype
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
TrimLLM: Progressive Layer Dropping for Domain-Specific LLMs—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
Trimming Down Large Spiking Vision Transformers via Heterogeneous Quantization Search—0
Reducing Computational Complexity of Neural Networks in Optical Channel Equalization: From Concepts to Implementation—0
What Makes a Good Dataset for Knowledge Distillation?—0
Reinforced Multi-Teacher Selection for Knowledge Distillation—0
Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence—0
A Deep Cascade Network for Unaligned Face Attribute Classification—0
Triple Sparsification of Graph Convolutional Networks without Sacrificing the Accuracy—0
Re-Parameterization of Lightweight Transformer for On-Device Speech Emotion Recognition—0
RepControlNet: ControlNet Reparameterization—0
Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization—0
Representation Transfer by Optimal Transport—0
Tuning Algorithms and Generators for Efficient Edge Inference—0
REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs—0
Residual Knowledge Distillation—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