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 426–450 of 1356 papers

TitleStatusHype
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models—0
ECoFLaP: Efficient Coarse-to-Fine Layer-Wise Pruning for Vision-Language Models—0
EDCompress: Energy-Aware Model Compression for Dataflows—0
Edge AI: Evaluation of Model Compression Techniques for Convolutional Neural Networks—0
Edge-AI for Agriculture: Lightweight Vision Models for Disease Detection in Resource-Limited Settings—0
Edge Deep Learning for Neural Implants—0
Edge-First Language Model Inference: Models, Metrics, and Tradeoffs—0
Edge-MultiAI: Multi-Tenancy of Latency-Sensitive Deep Learning Applications on Edge—0
Edge-Optimized Deep Learning & Pattern Recognition Techniques for Non-Intrusive Load Monitoring of Energy Time Series—0
Data-Free Quantization via Pseudo-label Filtering—0
Effective and Efficient Mixed Precision Quantization of Speech Foundation Models—0
Effective and Efficient One-pass Compression of Speech Foundation Models Using Sparsity-aware Self-pinching Gates—0
Effective Interplay between Sparsity and Quantization: From Theory to Practice—0
Effective Multi-Stage Training Model For Edge Computing Devices In Intrusion Detection—0
Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks—0
Efficient AI in Practice: Training and Deployment of Efficient LLMs for Industry Applications—0
Adaptive Learning of Tensor Network Structures—0
Data-Free Quantization via Mixed-Precision Compensation without Fine-Tuning—0
Efficient Apple Maturity and Damage Assessment: A Lightweight Detection Model with GAN and Attention Mechanism—0
Automated Inference of Graph Transformation Rules—0
Efficient classification using parallel and scalable compressed model and Its application on intrusion detection—0
Data-Free Knowledge Transfer: A Survey—0
Auto Graph Encoder-Decoder for Neural Network Pruning—0
Efficient DNN-Powered Software with Fair Sparse Models—0
A Low-Power Streaming Speech Enhancement Accelerator For Edge Devices—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