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 201–225 of 1356 papers

TitleStatusHype
The State of Sparsity in Deep Neural NetworksCode1
Learned Step Size QuantizationCode1
ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of MultipliersCode1
Discrimination-aware Channel Pruning for Deep Neural NetworksCode1
Dynamic Channel Pruning: Feature Boosting and SuppressionCode1
Verifiable Reinforcement Learning via Policy ExtractionCode1
To prune, or not to prune: exploring the efficacy of pruning for model compressionCode1
Ternary Weight NetworksCode1
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizeCode1
LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression—0
DipSVD: Dual-importance Protected SVD for Efficient LLM Compression—0
RLRC: Reinforcement Learning-based Recovery for Compressed Vision-Language-Action Models—0
Model compression using knowledge distillation with integrated gradients—0
EAQuant: Enhancing Post-Training Quantization for MoE Models via Expert-Aware OptimizationCode0
Simple is what you need for efficient and accurate medical image segmentationCode0
Attribution-guided Pruning for Compression, Circuit Discovery, and Targeted Correction in LLMsCode0
Advances in Small-Footprint Keyword Spotting: A Comprehensive Review of Efficient Models and AlgorithmsCode0
Post-Training Quantization for Video Matting—0
AWP: Activation-Aware Weight Pruning and Quantization with Projected Gradient Descent—0
Structured Pruning and Quantization for Learned Image CompressionCode0
INSIGHT: A Survey of In-Network Systems for Intelligent, High-Efficiency AI and Topology Optimization—0
Smooth Model Compression without Fine-Tuning—0
FLAT-LLM: Fine-grained Low-rank Activation Space Transformation for Large Language Model CompressionCode0
Effective and Efficient One-pass Compression of Speech Foundation Models Using Sparsity-aware Self-pinching Gates—0
Small Language Models: Architectures, Techniques, Evaluation, Problems and Future Adaptation—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