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 551–575 of 1356 papers

TitleStatusHype
A Theoretical Understanding of Neural Network Compression from Sparse Linear Approximation—0
A Half-Space Stochastic Projected Gradient Method for Group Sparsity Regularization—0
GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference—0
Convolutional Neural Network Compression via Dynamic Parameter Rank Pruning—0
Convolutional Neural Network Compression Based on Low-Rank Decomposition—0
Aggressive Post-Training Compression on Extremely Large Language Models—0
A Survey on Transformer Compression—0
Supervised domain adaptation for building extraction from off-nadir aerial images—0
Continuous Approximations for Improving Quantization Aware Training of LLMs—0
Context-aware deep model compression for edge cloud computing—0
A Survey on Model Compression and Acceleration for Pretrained Language Models—0
A Survey on Model Compression for Large Language Models—0
AfroXLMR-Comet: Multilingual Knowledge Distillation with Attention Matching for Low-Resource languages—0
NPAS: A Compiler-aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration—0
Gradient-Free Structured Pruning with Unlabeled Data—0
GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking—0
FSCNN: A Fast Sparse Convolution Neural Network Inference System—0
Frustratingly Easy Model Ensemble for Abstractive Summarization—0
From Word Vectors to Multimodal Embeddings: Techniques, Applications, and Future Directions For Large Language Models—0
From Large to Super-Tiny: End-to-End Optimization for Cost-Efficient LLMs—0
Conditional Teacher-Student Learning—0
Conditional Generative Data-free Knowledge Distillation—0
From Cloud to Edge: Rethinking Generative AI for Low-Resource Design Challenges—0
A Survey on Green Deep Learning—0
From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks—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