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 726–750 of 1356 papers

TitleStatusHype
Online Cross-Layer Knowledge Distillation on Graph Neural Networks with Deep Supervision—0
Legal-Tech Open Diaries: Lesson learned on how to develop and deploy light-weight models in the era of humongous Language Models—0
Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling—0
Sub-network Multi-objective Evolutionary Algorithm for Filter Pruning—0
Data-Model-Circuit Tri-Design for Ultra-Light Video Intelligence on Edge Devices—0
FIT: A Metric for Model Sensitivity—0
SeKron: A Decomposition Method Supporting Many Factorization Structures—0
Boosting Graph Neural Networks via Adaptive Knowledge Distillation—0
Deep learning model compression using network sensitivity and gradients—0
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models—0
Knowledge Distillation with Reptile Meta-Learning for Pretrained Language Model CompressionCode0
Multi-stage Progressive Compression of Conformer Transducer for On-device Speech Recognition—0
Match to Win: Analysing Sequences Lengths for Efficient Self-supervised Learning in Speech and Audio—0
Attacking Compressed Vision TransformersCode0
Analysis of Quantization on MLP-based Vision Models—0
Towards Sparsification of Graph Neural NetworksCode0
SaleNet: A low-power end-to-end CNN accelerator for sustained attention level evaluation using EEG—0
Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited EdgeCode0
Complexity-Driven CNN Compression for Resource-constrained Edge AI—0
Reducing Computational Complexity of Neural Networks in Optical Channel Equalization: From Concepts to Implementation—0
Robust and Large-Payload DNN Watermarking via Fixed, Distribution-Optimized, WeightsCode0
Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey—0
Enhancing Targeted Attack Transferability via Diversified Weight Pruning—0
An Algorithm-Hardware Co-Optimized Framework for Accelerating N:M Sparse Transformers—0
Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software DeploymentCode0
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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