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 26–50 of 1356 papers

TitleStatusHype
On Multilingual Encoder Language Model Compression for Low-Resource Languages—0
Edge-First Language Model Inference: Models, Metrics, and Tradeoffs—0
Is Quantum Optimization Ready? An Effort Towards Neural Network Compression using Adiabatic Quantum Computing—0
Saten: Sparse Augmented Tensor Networks for Post-Training Compression of Large Language Models—0
RanDeS: Randomized Delta Superposition for Multi-Model CompressionCode0
Consistent Quantity-Quality Control across Scenes for Deployment-Aware Gaussian SplattingCode1
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
Onboard Optimization and Learning: A Survey—0
Edge-Optimized Deep Learning & Pattern Recognition Techniques for Non-Intrusive Load Monitoring of Energy Time Series—0
Radio: Rate-Distortion Optimization for Large Language Model Compression—0
Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques—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
Aerial Image Classification in Scarce and Unconstrained Environments via Conformal Prediction—0
On-Device Qwen2.5: Efficient LLM Inference with Model Compression and Hardware Acceleration—0
From Large to Super-Tiny: End-to-End Optimization for Cost-Efficient LLMs—0
D^2MoE: Dual Routing and Dynamic Scheduling for Efficient On-Device MoE-based LLM Serving—0
ImPart: Importance-Aware Delta-Sparsification for Improved Model Compression and Merging in LLMsCode0
Efficient Reasoning Models: A SurveyCode3
Efficient Hybrid Language Model Compression through Group-Aware SSM Pruning—0
APSQ: Additive Partial Sum Quantization with Algorithm-Hardware Co-DesignCode0
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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