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 926–950 of 1356 papers

TitleStatusHype
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
Fast convolutional neural networks on FPGAs with hls4mlCode2
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
Improving Neural Network Efficiency via Post-Training Quantization With Adaptive Floating-PointCode1
Exploration and Estimation for Model Compression—0
Improve Object Detection with Feature-based Knowledge Distillation: Towards Accurate and Efficient DetectorsCode1
Model Compression via Hyper-Structure Network—0
Knowledge distillation via softmax regression representation learning—0
Post-Training Weighted Quantization of Neural Networks for Language Models—0
Task-Agnostic and Adaptive-Size BERT Compression—0
Can Students Outperform Teachers in Knowledge Distillation based Model Compression?—0
SACoD: Sensor Algorithm Co-Design Towards Efficient CNN-powered Intelligent PhlatCamCode0
Block Skim Transformer for Efficient Question Answering—0
A Half-Space Stochastic Projected Gradient Method for Group Sparsity Regularization—0
TwinDNN: A Tale of Two Deep Neural Networks—0
Dynamic Probabilistic Pruning: Training sparse networks based on stochastic and dynamic masking—0
EarlyBERT: Efficient BERT Training via Early-bird Lottery TicketsCode1
BinaryBERT: Pushing the Limit of BERT Quantization—0
Towards Zero-Shot Knowledge Distillation for Natural Language Processing—0
Show:102550
← PrevPage 38 of 55Next →
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