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 1301–1325 of 1356 papers

TitleStatusHype
Multi-Task Zipping via Layer-wise Neuron Sharing—0
DEEPEYE: A Compact and Accurate Video Comprehension at Terminal Devices Compressed with Quantization and Tensorization—0
Precise Box Score: Extract More Information from Datasets to Improve the Performance of Face Detection—0
Developing Far-Field Speaker System Via Teacher-Student Learning—0
Hybrid Binary Networks: Optimizing for Accuracy, Efficiency and MemoryCode0
Efficient Recurrent Neural Networks using Structured Matrices in FPGAs—0
Interpreting Deep Classifier by Visual Distillation of Dark Knowledge—0
Model compression via distillation and quantizationCode0
Paraphrasing Complex Network: Network Compression via Factor TransferCode0
Model compression for faster structural separation of macromolecules captured by Cellular Electron Cryo-Tomography—0
Don't encrypt the data; just approximate the model \ Towards Secure Transaction and Fair Pricing of Training Data—0
DNN Model Compression Under Accuracy Constraints—0
Adaptive Quantization of Neural Networks—0
Learning Deep and Compact Models for Gesture RecognitionCode0
StrassenNets: Deep Learning with a Multiplication BudgetCode0
Neural Regularized Domain Adaptation for Chinese Word Segmentation—0
Learning Efficient Object Detection Models with Knowledge Distillation—0
MicroExpNet: An Extremely Small and Fast Model For Expression Recognition From Face ImagesCode0
Improved Bayesian Compression—0
Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy—0
Weightless: Lossy Weight Encoding For Deep Neural Network CompressionCode0
A Survey of Model Compression and Acceleration for Deep Neural Networks—0
Compressing Low Precision Deep Neural Networks Using Sparsity-Induced Regularization in Ternary Networks—0
N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning—0
Learning Intrinsic Sparse Structures within Long Short-Term MemoryCode0
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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