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 376–400 of 1356 papers

TitleStatusHype
SwapNet: Efficient Swapping for DNN Inference on Edge AI Devices Beyond the Memory Budget—0
LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object DetectionCode2
TQCompressor: improving tensor decomposition methods in neural networks via permutationsCode0
CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks—0
Communication-Efficient Federated Learning through Adaptive Weight Clustering and Server-Side DistillationCode1
Large receptive field strategy and important feature extraction strategy in 3D object detection—0
Pruning for Protection: Increasing Jailbreak Resistance in Aligned LLMs Without Fine-TuningCode0
ELRT: Efficient Low-Rank Training for Compact Convolutional Neural Networks—0
SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for CompressionCode0
Model Compression Techniques in Biometrics Applications: A SurveyCode0
Dynamic DNNs and Runtime Management for Efficient Inference on Mobile/Embedded DevicesCode1
Convolutional Neural Network Compression via Dynamic Parameter Rank Pruning—0
Knowledge Translation: A New Pathway for Model CompressionCode0
FFSplit: Split Feed-Forward Network For Optimizing Accuracy-Efficiency Trade-off in Language Model Inference—0
Understanding LLMs: A Comprehensive Overview from Training to Inference—0
Retraining-free Model Quantization via One-Shot Weight-Coupling LearningCode1
Safety and Performance, Why Not Both? Bi-Objective Optimized Model Compression against Heterogeneous Attacks Toward AI Software DeploymentCode0
Data-Free Quantization via Pseudo-label Filtering—0
Unleashing Channel Potential: Space-Frequency Selection Convolution for SAR Object Detection—0
Explainability-Driven Leaf Disease Classification Using Adversarial Training and Knowledge Distillation—0
DMT: Comprehensive Distillation with Multiple Self-supervised Teachers—0
Integrating Fairness and Model Pruning Through Bi-level Optimization—0
Generative Model-based Feature Knowledge Distillation for Action RecognitionCode1
RankDVQA-mini: Knowledge Distillation-Driven Deep Video Quality Assessment—0
Unraveling Key Factors of 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