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 351–375 of 1356 papers

TitleStatusHype
LLM Inference Unveiled: Survey and Roofline Model InsightsCode4
Model Compression Method for S4 with Diagonal State Space Layers using Balanced Truncation—0
FinGPT-HPC: Efficient Pretraining and Finetuning Large Language Models for Financial Applications with High-Performance Computing—0
PromptKD: Distilling Student-Friendly Knowledge for Generative Language Models via Prompt TuningCode1
From Cloud to Edge: Rethinking Generative AI for Low-Resource Design Challenges—0
A Survey on Knowledge Distillation of Large Language ModelsCode5
Towards a tailored mixed-precision sub-8-bit quantization scheme for Gated Recurrent Units using Genetic Algorithms—0
Extraction of nonlinearity in neural networks with Koopman operator—0
Model Compression and Efficient Inference for Large Language Models: A Survey—0
Fast Vocabulary Transfer for Language Model CompressionCode1
Bayesian Deep Learning Via Expectation Maximization and Turbo Deep Approximate Message Passing—0
Memory-Efficient Vision Transformers: An Activation-Aware Mixed-Rank Compression Strategy—0
L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models—0
The Potential of AutoML for Recommender Systems—0
Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes—0
QuEST: Low-bit Diffusion Model Quantization via Efficient Selective FinetuningCode2
Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression—0
A Survey on Transformer Compression—0
Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation—0
Faster and Lighter LLMs: A Survey on Current Challenges and Way ForwardCode1
Mobile Fitting Room: On-device Virtual Try-on via Diffusion Models—0
EPSD: Early Pruning with Self-Distillation for Efficient Model Compression—0
RADIN: Souping on a Budget—0
Effective Multi-Stage Training Model For Edge Computing Devices In Intrusion Detection—0
Diffusion Model Compression for Image-to-Image Translation—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