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 476–500 of 1356 papers

TitleStatusHype
Enabling All In-Edge Deep Learning: A Literature Review—0
Efficient Supernet Training with Orthogonal Softmax for Scalable ASR Model Compression—0
Efficient Speech Representation Learning with Low-Bit Quantization—0
Efficient Recurrent Neural Networks using Structured Matrices in FPGAs—0
CoLLD: Contrastive Layer-to-layer Distillation for Compressing Multilingual Pre-trained Speech Encoders—0
Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy—0
Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels—0
Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization—0
Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity—0
Efficient Pruning of Text-to-Image Models: Insights from Pruning Stable Diffusion—0
Enhancing Inference Efficiency of Large Language Models: Investigating Optimization Strategies and Architectural Innovations—0
Efficient Point Cloud Classification via Offline Distillation Framework and Negative-Weight Self-Distillation Technique—0
Collaborative Teacher-Student Learning via Multiple Knowledge Transfer—0
Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review—0
Efficient Model Compression Techniques with FishLeg—0
Towards Feature Distribution Alignment and Diversity Enhancement for Data-Free Quantization—0
EPIM: Efficient Processing-In-Memory Accelerators based on Epitome—0
Applications of Knowledge Distillation in Remote Sensing: A Survey—0
Error-aware Quantization through Noise Tempering—0
ADC/DAC-Free Analog Acceleration of Deep Neural Networks with Frequency Transformation—0
Every Expert Matters: Towards Effective Knowledge Distillation for Mixture-of-Experts Language Models—0
Efficient Model Compression for Hierarchical Federated Learning—0
Efficient Model Compression for Bayesian Neural Networks—0
Efficient Memory Management for GPU-based Deep Learning Systems—0
ClusComp: A Simple Paradigm for Model Compression and Efficient Finetuning—0
Show:102550
← PrevPage 20 of 55Next →
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