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 201–250 of 1356 papers

TitleStatusHype
The State of Sparsity in Deep Neural NetworksCode1
Learned Step Size QuantizationCode1
ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of MultipliersCode1
Discrimination-aware Channel Pruning for Deep Neural NetworksCode1
Dynamic Channel Pruning: Feature Boosting and SuppressionCode1
Verifiable Reinforcement Learning via Policy ExtractionCode1
To prune, or not to prune: exploring the efficacy of pruning for model compressionCode1
Ternary Weight NetworksCode1
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizeCode1
LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression—0
DipSVD: Dual-importance Protected SVD for Efficient LLM Compression—0
RLRC: Reinforcement Learning-based Recovery for Compressed Vision-Language-Action Models—0
Model compression using knowledge distillation with integrated gradients—0
Simple is what you need for efficient and accurate medical image segmentationCode0
EAQuant: Enhancing Post-Training Quantization for MoE Models via Expert-Aware OptimizationCode0
Attribution-guided Pruning for Compression, Circuit Discovery, and Targeted Correction in LLMsCode0
Advances in Small-Footprint Keyword Spotting: A Comprehensive Review of Efficient Models and AlgorithmsCode0
Post-Training Quantization for Video Matting—0
AWP: Activation-Aware Weight Pruning and Quantization with Projected Gradient Descent—0
Structured Pruning and Quantization for Learned Image CompressionCode0
INSIGHT: A Survey of In-Network Systems for Intelligent, High-Efficiency AI and Topology Optimization—0
Smooth Model Compression without Fine-Tuning—0
FLAT-LLM: Fine-grained Low-rank Activation Space Transformation for Large Language Model CompressionCode0
Effective and Efficient One-pass Compression of Speech Foundation Models Using Sparsity-aware Self-pinching Gates—0
ResSVD: Residual Compensated SVD for Large Language Model Compression—0
Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks—0
Small Language Models: Architectures, Techniques, Evaluation, Problems and Future Adaptation—0
Pangu Light: Weight Re-Initialization for Pruning and Accelerating LLMs—0
Efficient Speech Translation through Model Compression and Knowledge DistillationCode0
Knowledge Grafting of Large Language ModelsCode0
Making deep neural networks work for medical audio: representation, compression and domain adaptation—0
Efficient and Workload-Aware LLM Serving via Runtime Layer Swapping and KV Cache Resizing—0
LatentLLM: Attention-Aware Joint Tensor Compression—0
Is Quantum Optimization Ready? An Effort Towards Neural Network Compression using Adiabatic Quantum Computing—0
Edge-First Language Model Inference: Models, Metrics, and Tradeoffs—0
On Multilingual Encoder Language Model Compression for Low-Resource Languages—0
Saten: Sparse Augmented Tensor Networks for Post-Training Compression of Large Language Models—0
RanDeS: Randomized Delta Superposition for Multi-Model CompressionCode0
Low-Complexity Inference in Continual Learning via Compressed Knowledge Transfer—0
KDH-MLTC: Knowledge Distillation for Healthcare Multi-Label Text Classification—0
Semantic Retention and Extreme Compression in LLMs: Can We Have Both?—0
Sponge Attacks on Sensing AI: Energy-Latency Vulnerabilities and Defense via Model Pruning—0
Edge-Optimized Deep Learning & Pattern Recognition Techniques for Non-Intrusive Load Monitoring of Energy Time Series—0
Onboard Optimization and Learning: A Survey—0
Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques—0
Radio: Rate-Distortion Optimization for Large Language Model Compression—0
Smart Environmental Monitoring of Marine Pollution using Edge AI—0
Towards Faster and More Compact Foundation Models for Molecular Property PredictionCode0
Low-Rank Matrix Approximation for Neural Network Compression—0
On-Device Qwen2.5: Efficient LLM Inference with Model Compression and Hardware Acceleration—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