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 451–500 of 1356 papers

TitleStatusHype
Compressing Convolutional Neural Networks via Factorized Convolutional FiltersCode0
Exploiting Kernel Sparsity and Entropy for Interpretable CNN CompressionCode0
FedSynth: Gradient Compression via Synthetic Data in Federated LearningCode0
Trio-ViT: Post-Training Quantization and Acceleration for Softmax-Free Efficient Vision TransformerCode0
From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model CompressionCode0
Compressed models are NOT miniature versions of large models—0
Artemis: HE-Aware Training for Efficient Privacy-Preserving Machine Learning—0
Comprehensive Survey of Model Compression and Speed up for Vision Transformers—0
Are We There Yet? A Measurement Study of Efficiency for LLM Applications on Mobile Devices—0
Comprehensive Study on Performance Evaluation and Optimization of Model Compression: Bridging Traditional Deep Learning and Large Language Models—0
ESPACE: Dimensionality Reduction of Activations for Model Compression—0
Compositionality Unlocks Deep Interpretable Models—0
A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques—0
Accelerating Very Deep Convolutional Networks for Classification and Detection—0
EPSD: Early Pruning with Self-Distillation for Efficient Model Compression—0
EoRA: Training-free Compensation for Compressed LLM with Eigenspace Low-Rank Approximation—0
CompMarkGS: Robust Watermarking for Compressed 3D Gaussian Splatting—0
Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks—0
Enhancing Targeted Attack Transferability via Diversified Weight Pruning—0
Complexity-Driven CNN Compression for Resource-constrained Edge AI—0
Architecture Compression—0
Compacting Deep Neural Networks for Internet of Things: Methods and Applications—0
Enhanced Sparsification via Stimulative Training—0
CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks—0
Energy-efficient Knowledge Distillation for Spiking Neural Networks—0
EncCluster: Scalable Functional Encryption in Federated Learning through Weight Clustering and Probabilistic Filters—0
Compact CNN Structure Learning by Knowledge Distillation—0
A Progressive Sub-Network Searching Framework for Dynamic Inference—0
A Deep Cascade Network for Unaligned Face Attribute Classification—0
Accelerating Machine Learning Primitives on Commodity Hardware—0
Enabling Deep Learning on Edge Devices through Filter Pruning and Knowledge Transfer—0
Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels—0
Enabling All In-Edge Deep Learning: A Literature Review—0
Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications—0
Communication-Efficient Federated Learning with Adaptive Compression under Dynamic Bandwidth—0
Enhancing Inference Efficiency of Large Language Models: Investigating Optimization Strategies and Architectural Innovations—0
Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models—0
ELRT: Efficient Low-Rank Training for Compact Convolutional Neural Networks—0
Communication-Efficient Distributed Online Learning with Kernels—0
A Privacy-Preserving-Oriented DNN Pruning and Mobile Acceleration Framework—0
E-LANG: Energy-Based Joint Inferencing of Super and Swift Language Models—0
EPIM: Efficient Processing-In-Memory Accelerators based on Epitome—0
Efficient Transformer Knowledge Distillation: A Performance Review—0
Error-aware Quantization through Noise Tempering—0
Approximability and Generalisation—0
Every Expert Matters: Towards Effective Knowledge Distillation for Mixture-of-Experts Language Models—0
Efficient Supernet Training with Orthogonal Softmax for Scalable ASR Model Compression—0
Efficient Speech Representation Learning with Low-Bit Quantization—0
Efficient Recurrent Neural Networks using Structured Matrices in FPGAs—0
CoLLD: Contrastive Layer-to-layer Distillation for Compressing Multilingual Pre-trained Speech Encoders—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