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 801–850 of 1356 papers

TitleStatusHype
Match to Win: Analysing Sequences Lengths for Efficient Self-supervised Learning in Speech and Audio—0
Matrix and tensor decompositions for training binary neural networks—0
Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons—0
Against Membership Inference Attack: Pruning is All You Need—0
MCNC: Manifold Constrained Network Compression—0
26ms Inference Time for ResNet-50: Towards Real-Time Execution of all DNNs on Smartphone—0
Robust Membership Encoding: Inference Attacks and Copyright Protection for Deep Learning—0
Memory- and Communication-Aware Model Compression for Distributed Deep Learning Inference on IoT—0
A "Network Pruning Network" Approach to Deep Model Compression—0
Memory-Efficient Vision Transformers: An Activation-Aware Mixed-Rank Compression Strategy—0
Memory-Friendly Scalable Super-Resolution via Rewinding Lottery Ticket Hypothesis—0
An Empirical Study of Low Precision Quantization for TinyML—0
Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains—0
An Empirical Investigation of Matrix Factorization Methods for Pre-trained Transformers—0
MICIK: MIning Cross-Layer Inherent Similarity Knowledge for Deep Model Compression—0
To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inference—0
A Multi-objective Complex Network Pruning Framework Based on Divide-and-conquer and Global Performance Impairment Ranking—0
MIMONet: Multi-Input Multi-Output On-Device Deep Learning—0
MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Tasks—0
Minimally Invasive Surgery for Sparse Neural Networks in Contrastive Manner—0
To Know Where We Are: Vision-Based Positioning in Outdoor Environments—0
Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal—0
Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework—0
An Embedded Deep Learning Object Detection Model For Traffic In Asian Countries—0
MLKD-BERT: Multi-level Knowledge Distillation for Pre-trained Language Models—0
MLPrune: Multi-Layer Pruning for Automated Neural Network Compression—0
Topology Distillation for Recommender System—0
An Efficient Sparse Inference Software Accelerator for Transformer-based Language Models on CPUs—0
MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases—0
Mobile Fitting Room: On-device Virtual Try-on via Diffusion Models—0
An Efficient Method of Training Small Models for Regression Problems with Knowledge Distillation—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
Accelerating deep neural networks for efficient scene understanding in automotive cyber-physical systems—0
Accelerating Deep Learning with Dynamic Data Pruning—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
torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation—0
Model compression using knowledge distillation with integrated gradients—0
Model Compression Using Optimal Transport—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