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 1026–1050 of 1356 papers

TitleStatusHype
Puppet-CNN: Input-Adaptive Convolutional Neural Networks with Model Compression using Ordinary Differential Equation—0
PURSUhInT: In Search of Informative Hint Points Based on Layer Clustering for Knowledge Distillation—0
Aerial Image Classification in Scarce and Unconstrained Environments via Conformal Prediction—0
QAPPA: Quantization-Aware Power, Performance, and Area Modeling of DNN Accelerators—0
QD-BEV : Quantization-aware View-guided Distillation for Multi-view 3D Object Detection—0
Adversarially Robust and Explainable Model Compression with On-Device Personalization for Text Classification—0
NPAS: A Compiler-aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration—0
Q-MambaIR: Accurate Quantized Mamba for Efficient Image Restoration—0
Adversarial Attacks on Machine Learning in Embedded and IoT Platforms—0
QTI Submission to DCASE 2021: residual normalization for device-imbalanced acoustic scene classification with efficient design—0
T-RECX: Tiny-Resource Efficient Convolutional neural networks with early-eXit—0
Quantizing YOLOv7: A Comprehensive Study—0
Quantum Neural Network Compression—0
Advancing IIoT with Over-the-Air Federated Learning: The Role of Iterative Magnitude Pruning—0
QuickNet: Maximizing Efficiency and Efficacy in Deep Architectures—0
QUIDAM: A Framework for Quantization-Aware DNN Accelerator and Model Co-Exploration—0
Quiver neural networks—0
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning—0
R2 Loss: Range Restriction Loss for Model Compression and Quantization—0
RADIN: Souping on a Budget—0
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
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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