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 1251–1275 of 1356 papers

TitleStatusHype
Decoupling Weight Regularization from Batch Size for Model Compression—0
Deep Collective Knowledge Distillation—0
Channel Compression: Rethinking Information Redundancy among Channels in CNN Architecture—0
Deep Compression of Neural Networks for Fault Detection on Tennessee Eastman Chemical Processes—0
10K is Enough: An Ultra-Lightweight Binarized Network for Infrared Small-Target Detection—0
DEEPEYE: A Compact and Accurate Video Comprehension at Terminal Devices Compressed with Quantization and Tensorization—0
You Only Prune Once: Designing Calibration-Free Model Compression With Policy Learning—0
Deep learning model compression using network sensitivity and gradients—0
Strategic Fusion Optimizes Transformer Compression—0
Deep Model Compression based on the Training History—0
Deep Model Compression: Distilling Knowledge from Noisy Teachers—0
Deep Model Compression Via Two-Stage Deep Reinforcement Learning—0
Neural Epitome Search for Architecture-Agnostic Network Compression—0
Streamlining Tensor and Network Pruning in PyTorch—0
DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices—0
Extending DeepSDF for automatic 3D shape retrieval and similarity transform estimation—0
Structured Bayesian Compression for Deep Neural Networks Based on The Turbo-VBI Approach—0
DeepTwist: Learning Model Compression via Occasional Weight Distortion—0
DeGAN : Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier—0
Delving Deep into Semantic Relation Distillation—0
Densely Distilling Cumulative Knowledge for Continual Learning—0
Order of Compression: A Systematic and Optimal Sequence to Combinationally Compress CNN—0
Dense Vision Transformer Compression with Few Samples—0
Dependency-Aware Semi-Structured Sparsity of GLU Variants in Large Language Models—0
Deploying Foundation Model Powered Agent Services: A Survey—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