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 1226–1250 of 1356 papers

TitleStatusHype
3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation—0
Model Slicing for Supporting Complex Analytics with Elastic Inference Cost and Resource ConstraintsCode0
Adversarial Robustness vs Model Compression, or Both?Code0
Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMMCode0
Real time backbone for semantic segmentation—0
Focused Quantization for Sparse CNNsCode0
Recurrent Convolution for Compact and Cost-Adjustable Neural Networks: An Empirical Study—0
The State of Sparsity in Deep Neural NetworksCode1
Learned Step Size QuantizationCode1
Efficient Memory Management for GPU-based Deep Learning Systems—0
Model Compression with Adversarial Robustness: A Unified Optimization FrameworkCode0
Architecture Compression—0
MICIK: MIning Cross-Layer Inherent Similarity Knowledge for Deep Model Compression—0
Tensorized Embedding Layers for Efficient Model CompressionCode0
Information-Theoretic Understanding of Population Risk Improvement with Model CompressionCode0
Intrinsically Sparse Long Short-Term Memory Networks—0
GASL: Guided Attention for Sparsity Learning in Deep Neural NetworksCode0
Exploring the 3D architectures of deep material network in data-driven multiscale mechanics—0
Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future Directions—0
Learning Efficient Detector with Semi-supervised Adaptive DistillationCode0
ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of MultipliersCode1
A Low Effort Approach to Structured CNN Design Using PCA—0
Exploiting Kernel Sparsity and Entropy for Interpretable CNN CompressionCode0
Stochastic Model Pruning via Weight Dropping Away and Back—0
Teacher-Student Compression with Generative Adversarial NetworksCode0
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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