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 651–675 of 1356 papers

TitleStatusHype
Inference Optimization of Foundation Models on AI Accelerators—0
Information-Theoretic GAN Compression with Variational Energy-based Model—0
Data-Free Knowledge Distillation Using Adversarially Perturbed OpenGL Shader Images—0
Infra-YOLO: Efficient Neural Network Structure with Model Compression for Real-Time Infrared Small Object Detection—0
InhibiDistilbert: Knowledge Distillation for a ReLU and Addition-based Transformer—0
Knowledge Distillation for Swedish NER models: A Search for Performance and Efficiency—0
FlatENN: Train Flat for Enhanced Fault Tolerance of Quantized Deep Neural Networks—0
Instance-Aware Group Quantization for Vision Transformers—0
Integral Pruning on Activations and Weights for Efficient Neural Networks—0
PublicCheck: Public Integrity Verification for Services of Run-time Deep Models—0
Interpreting Deep Classifier by Visual Distillation of Dark Knowledge—0
Redundancy and Concept Analysis for Code-trained Language Models—0
Intrinsically Sparse Long Short-Term Memory Networks—0
Investigation of Practical Aspects of Single Channel Speech Separation for ASR—0
Data-Model-Circuit Tri-Design for Ultra-Light Video Intelligence on Edge Devices—0
Is Quantum Optimization Ready? An Effort Towards Neural Network Compression using Adiabatic Quantum Computing—0
IteRABRe: Iterative Recovery-Aided Block Reduction—0
Iterative Compression of End-to-End ASR Model using AutoML—0
Decoupling Weight Regularization from Batch Size for Model Compression—0
It's always personal: Using Early Exits for Efficient On-Device CNN Personalisation—0
FIT: A Metric for Model Sensitivity—0
Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead—0
FinGPT-HPC: Efficient Pretraining and Finetuning Large Language Models for Financial Applications with High-Performance Computing—0
Joint Regularization on Activations and Weights for Efficient Neural Network Pruning—0
Fine-Tuning and Deploying Large Language Models Over Edges: Issues and Approaches—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