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 751–775 of 1356 papers

TitleStatusHype
Leveraging Different Learning Styles for Improved Knowledge Distillation in Biomedical Imaging—0
Leveraging Filter Correlations for Deep Model Compression—0
A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques—0
Light Field Compression Based on Implicit Neural Representation—0
Architecture Compression—0
Time-Correlated Sparsification for Efficient Over-the-Air Model Aggregation in Wireless Federated Learning—0
A Progressive Sub-Network Searching Framework for Dynamic Inference—0
Lightweight Convolutional Representations for On-Device Natural Language Processing—0
Lightweight Design and Optimization methods for DCNNs: Progress and Futures—0
Tiny but Accurate: A Pruned, Quantized and Optimized Memristor Crossbar Framework for Ultra Efficient DNN Implementation—0
A Privacy-Preserving-Oriented DNN Pruning and Mobile Acceleration Framework—0
LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression—0
Lipschitz Continuity Guided Knowledge Distillation—0
LIT: Block-wise Intermediate Representation Training for Model Compression—0
Approximability and Generalisation—0
Accelerating Framework of Transformer by Hardware Design and Model Compression Co-Optimization—0
Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy—0
Large Language Model Compression with Neural Architecture Search—0
Applications of Knowledge Distillation in Remote Sensing: A Survey—0
Locality-Sensitive Hashing for f-Divergences: Mutual Information Loss and Beyond—0
Localization-aware Channel Pruning for Object Detection—0
A Partial Regularization Method for Network Compression—0
LoCa: Logit Calibration for Knowledge Distillation—0
Local-Selective Feature Distillation for Single Image Super-Resolution—0
LORTSAR: Low-Rank Transformer for Skeleton-based Action Recognition—0
Show:102550
← PrevPage 31 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