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 451–500 of 1356 papers

TitleStatusHype
ClusComp: A Simple Paradigm for Model Compression and Efficient Finetuning—0
Efficient Memory Management for GPU-based Deep Learning Systems—0
MobiSR: Efficient On-Device Super-Resolution through Heterogeneous Mobile Processors—0
Efficient Model Compression for Hierarchical Federated Learning—0
Efficient Model Compression Techniques with FishLeg—0
Towards Feature Distribution Alignment and Diversity Enhancement for Data-Free Quantization—0
Feature Flow Regularization: Improving Structured Sparsity in Deep Neural Networks—0
FedCode: Communication-Efficient Federated Learning via Transferring Codebooks—0
Decoupling Weight Regularization from Batch Size for Model Compression—0
Efficient Pruning of Text-to-Image Models: Insights from Pruning Stable Diffusion—0
Debiased Distillation by Transplanting the Last Layer—0
Automatic Block-wise Pruning with Auxiliary Gating Structures for Deep Convolutional Neural Networks—0
Efficient Recurrent Neural Networks using Structured Matrices in FPGAs—0
Efficient Speech Representation Learning with Low-Bit Quantization—0
Data-Model-Circuit Tri-Design for Ultra-Light Video Intelligence on Edge Devices—0
Efficient Supernet Training with Orthogonal Softmax for Scalable ASR Model Compression—0
Data-Independent Structured Pruning of Neural Networks via Coresets—0
Automated Model Compression by Jointly Applied Pruning and Quantization—0
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models—0
E-LANG: Energy-Based Joint Inferencing of Super and Swift Language Models—0
ELRT: Efficient Low-Rank Training for Compact Convolutional Neural Networks—0
Communication-Efficient Distributed Online Learning with Kernels—0
Data-Free Quantization via Pseudo-label Filtering—0
Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models—0
Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications—0
Enabling All In-Edge Deep Learning: A Literature Review—0
Data-Free Quantization via Mixed-Precision Compensation without Fine-Tuning—0
Automated Inference of Graph Transformation Rules—0
Data-Free Knowledge Transfer: A Survey—0
Auto Graph Encoder-Decoder for Neural Network Pruning—0
A Low-Power Streaming Speech Enhancement Accelerator For Edge Devices—0
Acoustic Model Compression with MAP adaptation—0
Enhanced Sparsification via Stimulative Training—0
Data-Free Knowledge Distillation Using Adversarially Perturbed OpenGL Shader Images—0
Compacting Deep Neural Networks for Internet of Things: Methods and Applications—0
Enhancing Inference Efficiency of Large Language Models: Investigating Optimization Strategies and Architectural Innovations—0
AutoDistill: an End-to-End Framework to Explore and Distill Hardware-Efficient Language Models—0
Enhancing Targeted Attack Transferability via Diversified Weight Pruning—0
A Low Effort Approach to Structured CNN Design Using PCA—0
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification—0
FASTNav: Fine-tuned Adaptive Small-language-models Trained for Multi-point Robot Navigation—0
EPIM: Efficient Processing-In-Memory Accelerators based on Epitome—0
Data-Free Distillation of Language Model by Text-to-Text Transfer—0
Error-aware Quantization through Noise Tempering—0
26ms Inference Time for ResNet-50: Towards Real-Time Execution of all DNNs on Smartphone—0
Data-Free Adversarial Knowledge Distillation for Graph Neural Networks—0
AutoBSS: An Efficient Algorithm for Block Stacking Style Search—0
Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes—0
FedNILM: Applying Federated Learning to NILM Applications at the Edge—0
Data-Driven Compression of Convolutional Neural Networks—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