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 651–675 of 1356 papers

TitleStatusHype
Activation Map Adaptation for Effective Knowledge Distillation—0
Single-path Bit Sharing for Automatic Loss-aware Model Compression—0
Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments—0
Extending DeepSDF for automatic 3D shape retrieval and similarity transform estimation—0
DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices—0
AWP: Activation-Aware Weight Pruning and Quantization with Projected Gradient Descent—0
A Memory-Efficient Learning Framework for SymbolLevel Precoding with Quantized NN Weights—0
Neural Epitome Search for Architecture-Agnostic Network Compression—0
Deep Model Compression Via Two-Stage Deep Reinforcement Learning—0
Deep Model Compression: Distilling Knowledge from Noisy Teachers—0
Deep Model Compression based on the Training History—0
A Web-Based Solution for Federated Learning with LLM-Based Automation—0
AMD: Automatic Multi-step Distillation of Large-scale Vision Models—0
Deep learning model compression using network sensitivity and gradients—0
DEEPEYE: A Compact and Accurate Video Comprehension at Terminal Devices Compressed with Quantization and Tensorization—0
AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates—0
Deep Compression of Neural Networks for Fault Detection on Tennessee Eastman Chemical Processes—0
Automatic Mixed-Precision Quantization Search of BERT—0
Joint Regularization on Activations and Weights for Efficient Neural Network Pruning—0
Joint Neural Architecture Search and Quantization—0
Deep Collective Knowledge Distillation—0
Automatic Mapping of the Best-Suited DNN Pruning Schemes for Real-Time Mobile Acceleration—0
AMD: Adaptive Masked Distillation for Object Detection—0
Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks—0
It's always personal: Using Early Exits for Efficient On-Device CNN Personalisation—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