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 1001–1050 of 1356 papers

TitleStatusHype
Mobile Fitting Room: On-device Virtual Try-on via Diffusion Models—0
MoDeGPT: Modular Decomposition for Large Language Model Compression—0
Model Adaptation for Time Constrained Embodied Control—0
Model Blending for Text Classification—0
Model Compression—0
Model Compression and Efficient Inference for Large Language Models: A Survey—0
Model compression as constrained optimization, with application to neural nets. Part II: quantization—0
Model compression as constrained optimization, with application to neural nets. Part I: general framework—0
Model compression as constrained optimization, with application to neural nets. Part V: combining compressions—0
Scalable Model Compression by Entropy Penalized Reparameterization—0
Model Compression for DNN-based Speaker Verification Using Weight Quantization—0
Model compression for faster structural separation of macromolecules captured by Cellular Electron Cryo-Tomography—0
Model Compression for Resource-Constrained Mobile Robots—0
Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences—0
Model Compression Methods for YOLOv5: A Review—0
Model compression using knowledge distillation with integrated gradients—0
Model Compression Using Optimal Transport—0
Model Compression via Hyper-Structure Network—0
Model Compression via Symmetries of the Parameter Space—0
Model Compression with Generative Adversarial Networks—0
Model Compression with Multi-Task Knowledge Distillation for Web-scale Question Answering System—0
Model Compression with Two-stage Multi-teacher Knowledge Distillation for Web Question Answering System—0
Model Distillation with Knowledge Transfer from Face Classification to Alignment and Verification—0
On Cross-Layer Alignment for Model Fusion of Heterogeneous Neural Networks—0
A Light-weight Deep Human Activity Recognition Algorithm Using Multi-knowledge Distillation—0
Modular Transformers: Compressing Transformers into Modularized Layers for Flexible Efficient Inference—0
Modulating Regularization Frequency for Efficient Compression-Aware Model Training—0
MoQa: Rethinking MoE Quantization with Multi-stage Data-model Distribution Awareness—0
MPruner: Optimizing Neural Network Size with CKA-Based Mutual Information Pruning—0
MSP: An FPGA-Specific Mixed-Scheme, Multi-Precision Deep Neural Network Quantization Framework—0
MT-BioNER: Multi-task Learning for Biomedical Named Entity Recognition using Deep Bidirectional Transformers—0
Multi-Dimensional Pruning: A Unified Framework for Model Compression—0
Multi-head Knowledge Distillation for Model Compression—0
Multi-Precision Quantized Neural Networks via Encoding Decomposition of -1 and +1—0
MultiPruner: Balanced Structure Removal in Foundation Models—0
Multi-stage Progressive Compression of Conformer Transducer for On-device Speech Recognition—0
Multi-task Learning Approach for Modulation and Wireless Signal Classification for 5G and Beyond: Edge Deployment via Model Compression—0
Multi-Task Semantic Communications via Large Models—0
Multi-Task Zipping via Layer-wise Neuron Sharing—0
MWQ: Multiscale Wavelet Quantized Neural Networks—0
N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning—0
N-Ary Quantization for CNN Model Compression and Inference Acceleration—0
NAS-BERT: Task-Agnostic and Adaptive-Size BERT Compression with Neural Architecture Search—0
Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future Directions—0
NeR-VCP: A Video Content Protection Method Based on Implicit Neural Representation—0
Reconstructing Pruned Filters using Cheap Spatial Transformations—0
Network Implosion: Effective Model Compression for ResNets via Static Layer Pruning and Retraining—0
Network Pruning for Low-Rank Binary Index—0
Network Pruning for Low-Rank Binary Indexing—0
Neural 3D Scene Compression via Model Compression—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