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 101–125 of 1356 papers

TitleStatusHype
Communication-Efficient Federated Learning through Adaptive Weight Clustering and Server-Side DistillationCode1
An Empirical Study of CLIP for Text-based Person SearchCode1
Composable Interventions for Language ModelsCode1
FedUKD: Federated UNet Model with Knowledge Distillation for Land Use Classification from Satellite and Street ViewsCode1
FIMA-Q: Post-Training Quantization for Vision Transformers by Fisher Information Matrix ApproximationCode1
Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural NetworksCode1
A Survey on Dynamic Neural Networks: from Computer Vision to Multi-modal Sensor FusionCode1
An Information Theory-inspired Strategy for Automatic Network PruningCode1
Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural NetworksCode1
Generative Model-based Feature Knowledge Distillation for Action RecognitionCode1
Head Network Distillation: Splitting Distilled Deep Neural Networks for Resource-Constrained Edge Computing SystemsCode1
HiNeRV: Video Compression with Hierarchical Encoding-based Neural RepresentationCode1
Hyper-Compression: Model Compression via HyperfunctionCode1
Implicit Regularization via Neural Feature AlignmentCode1
Improving Post Training Neural Quantization: Layer-wise Calibration and Integer ProgrammingCode1
Initialization and Regularization of Factorized Neural LayersCode1
Consistent Quantity-Quality Control across Scenes for Deployment-Aware Gaussian SplattingCode1
KD-MRI: A knowledge distillation framework for image reconstruction and image restoration in MRI workflowCode1
3DG-STFM: 3D Geometric Guided Student-Teacher Feature MatchingCode1
Accurate Retraining-free Pruning for Pretrained Encoder-based Language ModelsCode1
AD-KD: Attribution-Driven Knowledge Distillation for Language Model CompressionCode1
A Real-time Low-cost Artificial Intelligence System for Autonomous Spraying in Palm PlantationsCode1
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated LearningCode1
Class Attention Transfer Based Knowledge DistillationCode1
Model LEGO: Creating Models Like Disassembling and Assembling Building BlocksCode1
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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