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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

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Showing 1301–1325 of 1356 papers

TitleStatusHype
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
AMC: AutoML for Model Compression and Acceleration on Mobile DevicesCode2
Model compression for faster structural separation of macromolecules captured by Cellular Electron Cryo-Tomography—0
DNN Model Compression Under Accuracy Constraints—0
Don't encrypt the data; just approximate the model \ Towards Secure Transaction and Fair Pricing of Training Data—0
Adaptive Quantization of Neural Networks—0
Learning Deep and Compact Models for Gesture RecognitionCode0
StrassenNets: Deep Learning with a Multiplication BudgetCode0
Learning Efficient Object Detection Models with Knowledge Distillation—0
Neural Regularized Domain Adaptation for Chinese Word Segmentation—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
Data-Free Knowledge Distillation for Deep Neural NetworksCode2
To prune, or not to prune: exploring the efficacy of pruning for model compressionCode1
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
A Deep Cascade Network for Unaligned Face Attribute Classification—0
Model Distillation with Knowledge Transfer from Face Classification to Alignment and Verification—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