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 501–525 of 1356 papers

TitleStatusHype
Explainability-Driven Leaf Disease Classification Using Adversarial Training and Knowledge Distillation—0
Explaining Sequence-Level Knowledge Distillation as Data-Augmentation for Neural Machine Translation—0
FFSplit: Split Feed-Forward Network For Optimizing Accuracy-Efficiency Trade-off in Language Model Inference—0
FLOPs as a Direct Optimization Objective for Learning Sparse Neural Networks—0
Are We There Yet? A Measurement Study of Efficiency for LLM Applications on Mobile Devices—0
Exploiting Non-Linear Redundancy for Neural Model Compression—0
Exploiting random projections and sparsity with random forests and gradient boosting methods -- Application to multi-label and multi-output learning, random forest model compression and leveraging input sparsity—0
Exploration and Estimation for Model Compression—0
Exploring compressibility of transformer based text-to-music (TTM) models—0
Artemis: HE-Aware Training for Efficient Privacy-Preserving Machine Learning—0
From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks—0
Data-Driven Compression of Convolutional Neural Networks—0
A Unified Knowledge Distillation Framework for Deep Directed Graphical Models—0
DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer—0
DARC: Differentiable ARchitecture Compression—0
A Unified Framework of DNN Weight Pruning and Weight Clustering/Quantization Using ADMM—0
Aligned Weight Regularizers for Pruning Pretrained Neural Networks—0
DARB: A Density-Aware Regular-Block Pruning for Deep Neural Networks—0
D^2MoE: Dual Routing and Dynamic Scheduling for Efficient On-Device MoE-based LLM Serving—0
A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks—0
CURing Large Models: Compression via CUR Decomposition—0
CSTAR: Towards Compact and STructured Deep Neural Networks with Adversarial Robustness—0
Augmenting Knowledge Distillation With Peer-To-Peer Mutual Learning For Model Compression—0
Artificial Neural Networks for Photonic Applications: From Algorithms to Implementation—0
CrossQuant: A Post-Training Quantization Method with Smaller Quantization Kernel for Precise Large Language Model Compression—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