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 251–275 of 1356 papers

TitleStatusHype
Comprehensive Survey of Model Compression and Speed up for Vision Transformers—0
Are We There Yet? A Measurement Study of Efficiency for LLM Applications on Mobile Devices—0
Compressed models are NOT miniature versions of large models—0
Artemis: HE-Aware Training for Efficient Privacy-Preserving Machine Learning—0
A Novel Architecture Slimming Method for Network Pruning and Knowledge Distillation—0
Adaptive Learning of Tensor Network Structures—0
Characterizing the Accuracy -- Efficiency Trade-off of Low-rank Decomposition in Language Models—0
Accelerating Framework of Transformer by Hardware Design and Model Compression Co-Optimization—0
CSTAR: Towards Compact and STructured Deep Neural Networks with Adversarial Robustness—0
Channel Compression: Rethinking Information Redundancy among Channels in CNN Architecture—0
An Improving Framework of regularization for Network Compression—0
Order of Compression: A Systematic and Optimal Sequence to Combinationally Compress CNN—0
Adaptive Quantization of Neural Networks—0
CrossQuant: A Post-Training Quantization Method with Smaller Quantization Kernel for Precise Large Language Model Compression—0
CURing Large Models: Compression via CUR Decomposition—0
Accelerating deep neural networks for efficient scene understanding in automotive cyber-physical systems—0
Adaptive Neural Connections for Sparsity Learning—0
Croesus: Multi-Stage Processing and Transactions for Video-Analytics in Edge-Cloud Systems—0
Cascaded channel pruning using hierarchical self-distillation—0
Can We Find Strong Lottery Tickets in Generative Models?—0
A New Clustering-Based Technique for the Acceleration of Deep Convolutional Networks—0
Cross-Channel Intragroup Sparsity Neural Network—0
Can Students Outperform Teachers in Knowledge Distillation based Model Compression?—0
Can Students Beyond The Teacher? Distilling Knowledge from Teacher's Bias—0
A "Network Pruning Network" Approach to Deep 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