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 1201–1225 of 1356 papers

TitleStatusHype
Weight Normalization based Quantization for Deep Neural Network Compression—0
COP: Customized Deep Model Compression via Regularized Correlation-Based Filter-Level PruningCode0
Joint Regularization on Activations and Weights for Efficient Neural Network Pruning—0
Scalable Model Compression by Entropy Penalized Reparameterization—0
Membership Privacy for Machine Learning Models Through Knowledge Transfer—0
Does Learning Require Memorization? A Short Tale about a Long Tail—0
Network Implosion: Effective Model Compression for ResNets via Static Layer Pruning and Retraining—0
Deep Face Recognition Model Compression via Knowledge Transfer and Distillation—0
Compressing Convolutional Neural Networks via Factorized Convolutional FiltersCode0
Cross Domain Model Compression by Structurally Weight Sharing—0
Multi-Precision Quantized Neural Networks via Encoding Decomposition of -1 and +1—0
HadaNets: Flexible Quantization Strategies for Neural Networks—0
Bayesian Tensorized Neural Networks with Automatic Rank SelectionCode0
Learning Low-Rank Approximation for CNNs—0
Structured Compression by Weight Encryption for Unstructured Pruning and Quantization—0
DARC: Differentiable ARchitecture Compression—0
Compressed Learning of Deep Neural Networks for OpenCL-Capable Embedded SystemsCode0
Dream Distillation: A Data-Independent Model Compression Framework—0
Network Pruning for Low-Rank Binary Indexing—0
Play and Prune: Adaptive Filter Pruning for Deep Model CompressionCode0
2-bit Model Compression of Deep Convolutional Neural Network on ASIC Engine for Image Retrieval—0
Creating Lightweight Object Detectors with Model Compression for Deployment on Edge Devices—0
26ms Inference Time for ResNet-50: Towards Real-Time Execution of all DNNs on Smartphone—0
Toward Extremely Low Bit and Lossless Accuracy in DNNs with Progressive ADMM—0
Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network—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