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 826–850 of 1356 papers

TitleStatusHype
High-fidelity 3D Model Compression based on Key SpheresCode0
PCEE-BERT: Accelerating BERT Inference via Patient and Confident Early Exiting—0
UDC: Unified DNAS for Compressible TinyML Models—0
DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI ScaleCode0
ThreshNet: An Efficient DenseNet Using Threshold Mechanism to Reduce ConnectionsCode0
Two-Pass End-to-End ASR Model Compression—0
The Effect of Model Compression on Fairness in Facial Expression Recognition—0
HODEC: Towards Efficient High-Order DEcomposed Convolutional Neural Networks—0
Dreaming To Prune Image Deraining Networks—0
Multi-Dimensional Model Compression of Vision TransformerCode0
Conditional Generative Data-free Knowledge Distillation—0
Croesus: Multi-Stage Processing and Transactions for Video-Analytics in Edge-Cloud Systems—0
Data-Free Knowledge Transfer: A Survey—0
Automatic Mixed-Precision Quantization Search of BERT—0
LegoDNN: Block-grained Scaling of Deep Neural Networks for Mobile Vision—0
From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model CompressionCode0
Knowledge Distillation for Object Detection via Rank Mimicking and Prediction-guided Feature Imitation—0
Low-rank Tensor Decomposition for Compression of Convolutional Neural Networks Using Funnel Regularization—0
Finding Deviated Behaviors of the Compressed DNN Models for Image ClassificationsCode0
Toward Real-World Voice Disorder Classification—0
Shapeshifter: a Parameter-efficient Transformer using Factorized Reshaped MatricesCode0
Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations—0
FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorization—0
Exploring Low-Cost Transformer Model Compression for Large-Scale Commercial Reply Suggestions—0
Accelerating Deep Learning with Dynamic Data Pruning—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