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 376–400 of 1356 papers

TitleStatusHype
A Survey of Small 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
Preview-based Category Contrastive Learning for Knowledge Distillation—0
QIANets: Quantum-Integrated Adaptive Networks for Reduced Latency and Improved Inference Times in CNN ModelsCode0
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
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
Trainable pruned ternary quantization for medical signal classification modelsCode0
Compressing Recurrent Neural Networks for FPGA-accelerated Implementation in Fluorescence Lifetime Imaging—0
Aggressive Post-Training Compression on Extremely Large Language Models—0
InfantCryNet: A Data-driven Framework for Intelligent Analysis of Infant Cries—0
Value-Based Deep Multi-Agent Reinforcement Learning with Dynamic Sparse Training—0
General Compression Framework for Efficient Transformer Object Tracking—0
Enhancing Knowledge Distillation of Large Language Models through Efficient Multi-Modal Distribution AlignmentCode0
Applications of Knowledge Distillation in Remote Sensing: A Survey—0
ELSA: Exploiting Layer-wise N:M Sparsity for Vision Transformer AccelerationCode0
Privacy-Preserving SAM Quantization for Efficient Edge Intelligence in Healthcare—0
NVRC: Neural Video Representation 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