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 876–900 of 1356 papers

TitleStatusHype
Multi-Precision Quantized Neural Networks via Encoding Decomposition of -1 and +1—0
MultiPruner: Balanced Structure Removal in Foundation Models—0
Multi-stage Progressive Compression of Conformer Transducer for On-device Speech Recognition—0
Multi-task Learning Approach for Modulation and Wireless Signal Classification for 5G and Beyond: Edge Deployment via Model Compression—0
Multi-Task Semantic Communications via Large Models—0
Multi-Task Zipping via Layer-wise Neuron Sharing—0
MWQ: Multiscale Wavelet Quantized Neural Networks—0
N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning—0
Analysis of Quantization on MLP-based Vision Models—0
N-Ary Quantization for CNN Model Compression and Inference Acceleration—0
NAS-BERT: Task-Agnostic and Adaptive-Size BERT Compression with Neural Architecture Search—0
Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future Directions—0
NeR-VCP: A Video Content Protection Method Based on Implicit Neural Representation—0
Reconstructing Pruned Filters using Cheap Spatial Transformations—0
Network Implosion: Effective Model Compression for ResNets via Static Layer Pruning and Retraining—0
Network Pruning for Low-Rank Binary Index—0
Network Pruning for Low-Rank Binary Indexing—0
Weight Normalization based Quantization for Deep Neural Network Compression—0
Neural 3D Scene Compression via Model Compression—0
Neural Architecture Codesign for Fast Bragg Peak Analysis—0
ACAM-KD: Adaptive and Cooperative Attention Masking for Knowledge Distillation—0
Neural Network Compression for Noisy Storage Devices—0
Neural Network Compression using Binarization and Few Full-Precision Weights—0
Neural Network Compression Via Sparse Optimization—0
Neural Network Pruning by Cooperative Coevolution—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