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 351–375 of 1356 papers

TitleStatusHype
A Web-Based Solution for Federated Learning with LLM-Based Automation—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
Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network—0
AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates—0
Deep learning model compression using network sensitivity and gradients—0
Deploying Foundation Model Powered Agent Services: A Survey—0
Domain Adaptation Regularization for Spectral Pruning—0
Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing—0
Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey—0
Developing Far-Field Speaker System Via Teacher-Student Learning—0
Differentiable Architecture Compression—0
Differentiable Feature Aggregation Search for Knowledge Distillation—0
Differentiable Mask for Pruning Convolutional and Recurrent Networks—0
AMD: Adaptive Masked Distillation for Object Detection—0
DEEPEYE: A Compact and Accurate Video Comprehension at Terminal Devices Compressed with Quantization and Tensorization—0
Differentiable Sparsification for Deep Neural Networks—0
Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks—0
Differentiable Sparsification for Deep Neural Networks—0
Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization—0
Differential Privacy Meets Federated Learning under Communication Constraints—0
Automatic Mixed-Precision Quantization Search of BERT—0
Dimensionality Reduced Training by Pruning and Freezing Parts of a Deep Neural Network, a Survey—0
Deep Compression of Neural Networks for Fault Detection on Tennessee Eastman Chemical Processes—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