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 851–875 of 1356 papers

TitleStatusHype
Toward Extremely Low Bit and Lossless Accuracy in DNNs with Progressive ADMM—0
Model Compression via Hyper-Structure Network—0
Model Compression via Symmetries of the Parameter Space—0
Toward Real-World Voice Disorder Classification—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
An Effective Information Theoretic Framework for Channel Pruning—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
Towards Accurate Post-Training Quantization for Vision Transformer—0
A Light-weight Deep Human Activity Recognition Algorithm Using Multi-knowledge Distillation—0
Towards a tailored mixed-precision sub-8-bit quantization scheme for Gated Recurrent Units using Genetic Algorithms—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
Towards Better Parameter-Efficient Fine-Tuning for Large Language Models: A Position Paper—0
Multi-Dimensional Pruning: A Unified Framework for Model Compression—0
Towards Building a Real Time Mobile Device Bird Counting System Through Synthetic Data Training and Model Compression—0
Multi-head Knowledge Distillation for Model Compression—0
An Automatic and Efficient BERT Pruning for Edge AI Systems—0
Towards domain generalisation in ASR with elitist sampling and ensemble knowledge distillation—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