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 176–200 of 1356 papers

TitleStatusHype
From Word Vectors to Multimodal Embeddings: Techniques, Applications, and Future Directions For Large Language Models—0
Change Is the Only Constant: Dynamic LLM Slicing based on Layer RedundancyCode0
Efficient Model Compression for Bayesian Neural Networks—0
ML Research BenchmarkCode0
LLMCBench: Benchmarking Large Language Model Compression for Efficient DeploymentCode1
EoRA: Training-free Compensation for Compressed LLM with Eigenspace Low-Rank Approximation—0
A Survey of Small Language Models—0
SWITCH: Studying with Teacher for Knowledge Distillation of Large Language Models—0
Beware of Calibration Data for Pruning Large Language Models—0
Towards Effective Data-Free Knowledge Distillation via Diverse Diffusion AugmentationCode0
Self-calibration for Language Model Quantization and Pruning—0
Identifying Sub-networks in Neural Networks via Functionally Similar Representations—0
EvoPress: Towards Optimal Dynamic Model Compression via Evolutionary SearchCode1
Preview-based Category Contrastive Learning for Knowledge Distillation—0
QIANets: Quantum-Integrated Adaptive Networks for Reduced Latency and Improved Inference Times in CNN ModelsCode0
SLiM: One-shot Quantization and Sparsity with Low-rank Approximation for LLM Weight CompressionCode1
What is Left After Distillation? How Knowledge Transfer Impacts Fairness and Bias—0
CrossQuant: A Post-Training Quantization Method with Smaller Quantization Kernel for Precise Large Language Model Compression—0
Large Language Model Compression with Neural Architecture Search—0
QT-DoG: Quantization-aware Training for Domain GeneralizationCode1
SpaLLM: Unified Compressive Adaptation of Large Language Models with Sketching—0
ESPACE: Dimensionality Reduction of Activations for Model Compression—0
Continuous Approximations for Improving Quantization Aware Training of LLMs—0
Geometry is All You Need: A Unified Taxonomy of Matrix and Tensor Factorization for Compression of Generative Language Models—0
Basis Sharing: Cross-Layer Parameter Sharing for Large Language Model CompressionCode1
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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