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 426–450 of 1356 papers

TitleStatusHype
Empirical Evaluation of Deep Learning Model Compression Techniques on the WaveNet VocoderCode0
ELSA: Exploiting Layer-wise N:M Sparsity for Vision Transformer AccelerationCode0
An exploration of the effect of quantisation on energy consumption and inference time of StarCoder2Code0
Causal Explanation of Convolutional Neural NetworksCode0
StrassenNets: Deep Learning with a Multiplication BudgetCode0
Einconv: Exploring Unexplored Tensor Network Decompositions for Convolutional Neural NetworksCode0
Improved Knowledge Distillation via Full Kernel Matrix TransferCode0
Compressing Convolutional Neural Networks via Factorized Convolutional FiltersCode0
Efficient model compression with Random Operation Access Specific Tile (ROAST) hashingCode0
Efficient Speech Translation through Model Compression and Knowledge DistillationCode0
Systematic Outliers in Large Language ModelsCode0
Exploring Unexplored Tensor Network Decompositions for Convolutional Neural NetworksCode0
Tensorized Embedding Layers for Efficient Model CompressionCode0
Learning Accurate Performance Predictors for Ultrafast Automated Model CompressionCode0
On Model Compression for Neural Networks: Framework, Algorithm, and Convergence GuaranteeCode0
Compressed models are NOT miniature versions of large models—0
Artemis: HE-Aware Training for Efficient Privacy-Preserving Machine Learning—0
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
Comprehensive Study on Performance Evaluation and Optimization of Model Compression: Bridging Traditional Deep Learning and Large Language Models—0
Compositionality Unlocks Deep Interpretable Models—0
A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques—0
Accelerating Very Deep Convolutional Networks for Classification and Detection—0
CompMarkGS: Robust Watermarking for Compressed 3D Gaussian Splatting—0
Complexity-Driven CNN Compression for Resource-constrained Edge AI—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