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 976–1000 of 1356 papers

TitleStatusHype
Lottery Ticket Preserves Weight Correlation: Is It Desirable or Not?—0
Neural Network Compression for Noisy Storage Devices—0
Robustness in Compressed Neural Networks for Object Detection—0
Compressed Object DetectionCode0
It's always personal: Using Early Exits for Efficient On-Device CNN Personalisation—0
AACP: Model Compression by Accurate and Automatic Channel Pruning—0
Deep Model Compression based on the Training History—0
AdaSpring: Context-adaptive and Runtime-evolutionary Deep Model Compression for Mobile Applications—0
Differential Privacy Meets Federated Learning under Communication Constraints—0
Collaborative Teacher-Student Learning via Multiple Knowledge Transfer—0
Deep Compression of Neural Networks for Fault Detection on Tennessee Eastman Chemical Processes—0
Model Compression for Domain Adaptation through Causal Effect EstimationCode0
Single-path Bit Sharing for Automatic Loss-aware Model Compression—0
Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks—0
Adversarially Robust and Explainable Model Compression with On-Device Personalization for Text Classification—0
On-Device Document Classification using multimodal features—0
Block Skim Transformer for Efficient Question Answering—0
TwinDNN: A Tale of Two Deep Neural Networks—0
Model Compression via Hyper-Structure Network—0
Can Students Outperform Teachers in Knowledge Distillation based Model Compression?—0
Knowledge distillation via softmax regression representation learning—0
SACoD: Sensor Algorithm Co-Design Towards Efficient CNN-powered Intelligent PhlatCamCode0
Dynamic Probabilistic Pruning: Training sparse networks based on stochastic and dynamic masking—0
Exploration and Estimation for Model Compression—0
Post-Training Weighted Quantization of Neural Networks for Language 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