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 501–525 of 1356 papers

TitleStatusHype
Is Modularity Transferable? A Case Study through the Lens of Knowledge DistillationCode0
Dense Vision Transformer Compression with Few Samples—0
Are Compressed Language Models Less Subgroup Robust?Code0
Tiny Models are the Computational Saver for Large ModelsCode0
Order of Compression: A Systematic and Optimal Sequence to Combinationally Compress CNN—0
Magic for the Age of Quantized DNNs—0
Advancing IIoT with Over-the-Air Federated Learning: The Role of Iterative Magnitude Pruning—0
DiPaCo: Distributed Path Composition—0
BRIEDGE: EEG-Adaptive Edge AI for Multi-Brain to Multi-Robot Interaction—0
Adversarial Fine-tuning of Compressed Neural Networks for Joint Improvement of Robustness and EfficiencyCode0
Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons—0
Enhanced Sparsification via Stimulative Training—0
Optimal Policy Sparsification and Low Rank Decomposition for Deep Reinforcement Learning—0
DyCE: Dynamically Configurable Exiting for Deep Learning Compression and Real-time ScalingCode0
Towards efficient deep autoencoders for multivariate time series anomaly detection—0
Differentially Private Knowledge Distillation via Synthetic Text GenerationCode0
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
From Cloud to Edge: Rethinking Generative AI for Low-Resource Design Challenges—0
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
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
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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