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 601–625 of 1356 papers

TitleStatusHype
Greener yet Powerful: Taming Large Code Generation Models with Quantization—0
Group channel pruning and spatial attention distilling for object detection—0
GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking—0
Atrial Fibrillation Detection Using Weight-Pruned, Log-Quantised Convolutional Neural Networks—0
Fragile Mastery: Are Domain-Specific Trade-Offs Undermining On-Device Language Models?—0
HadaNets: Flexible Quantization Strategies for Neural Networks—0
HALOC: Hardware-Aware Automatic Low-Rank Compression for Compact Neural Networks—0
Conditional Automated Channel Pruning for Deep Neural Networks—0
HCE: Improving Performance and Efficiency with Heterogeneously Compressed Neural Network Ensemble—0
A flexible, extensible software framework for model compression based on the LC algorithm—0
HFSP: A Hardware-friendly Soft Pruning Framework for Vision Transformers—0
HideNseek: Federated Lottery Ticket via Server-side Pruning and Sign Supermask—0
Cross-Channel Intragroup Sparsity Neural Network—0
Attention Sinks and Outlier Features: A 'Catch, Tag, and Release' Mechanism for Embeddings—0
Improving Acoustic Scene Classification in Low-Resource Conditions—0
HODEC: Towards Efficient High-Order DEcomposed Convolutional Neural Networks—0
ConaCLIP: Exploring Distillation of Fully-Connected Knowledge Interaction Graph for Lightweight Text-Image Retrieval—0
How and When Adversarial Robustness Transfers in Knowledge Distillation?—0
On the Impact of Calibration Data in Post-training Quantization and Pruning—0
Deep Face Recognition Model Compression via Knowledge Transfer and Distillation—0
How to Explain Neural Networks: an Approximation Perspective—0
How to Select One Among All ? An Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language Understanding—0
Formalizing Generalization and Robustness of Neural Networks to Weight Perturbations—0
Aerial Image Classification in Scarce and Unconstrained Environments via Conformal Prediction—0
Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations—0
Show:102550
← PrevPage 25 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