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 251–300 of 1356 papers

TitleStatusHype
Aerial Image Classification in Scarce and Unconstrained Environments via Conformal Prediction—0
From Large to Super-Tiny: End-to-End Optimization for Cost-Efficient LLMs—0
ImPart: Importance-Aware Delta-Sparsification for Improved Model Compression and Merging in LLMsCode0
D^2MoE: Dual Routing and Dynamic Scheduling for Efficient On-Device MoE-based LLM Serving—0
Efficient Hybrid Language Model Compression through Group-Aware SSM Pruning—0
APSQ: Additive Partial Sum Quantization with Algorithm-Hardware Co-DesignCode0
Two is Better than One: Efficient Ensemble Defense for Robust and Compact Models—0
Thanos: A Block-wise Pruning Algorithm for Efficient Large Language Model CompressionCode0
Compression Laws for Large Language Models—0
RingMoE: Mixture-of-Modality-Experts Multi-Modal Foundation Models for Universal Remote Sensing Image Interpretation—0
Compositionality Unlocks Deep Interpretable Models—0
Random Conditioning with Distillation for Data-Efficient Diffusion Model Compression—0
Penrose Tiled Low-Rank Compression and Section-Wise Q&A Fine-Tuning: A General Framework for Domain-Specific Large Language Model Adaptation—0
Multi-Task Semantic Communications via Large Models—0
Delving Deep into Semantic Relation Distillation—0
MoQa: Rethinking MoE Quantization with Multi-stage Data-model Distribution Awareness—0
Boosting Large Language Models with Mask Fine-TuningCode0
Q-MambaIR: Accurate Quantized Mamba for Efficient Image Restoration—0
A Low-Power Streaming Speech Enhancement Accelerator For Edge Devices—0
Temporal Action Detection Model Compression by Progressive Block Drop—0
Large Language Model Compression via the Nested Activation-Aware Decomposition—0
InhibiDistilbert: Knowledge Distillation for a ReLU and Addition-based Transformer—0
CompMarkGS: Robust Watermarking for Compressed 3D Gaussian Splatting—0
ClusComp: A Simple Paradigm for Model Compression and Efficient Finetuning—0
Fragile Mastery: Are Domain-Specific Trade-Offs Undermining On-Device Language Models?—0
Sometimes Painful but Certainly Promising: Feasibility and Trade-offs of Language Model Inference at the Edge—0
Position-Aware Depth Decay Decoding (D^3): Boosting Large Language Model Inference Efficiency—0
Are We There Yet? A Measurement Study of Efficiency for LLM Applications on Mobile Devices—0
Towards Superior Quantization Accuracy: A Layer-sensitive Approach—0
IteRABRe: Iterative Recovery-Aided Block Reduction—0
ACAM-KD: Adaptive and Cooperative Attention Masking for Knowledge Distillation—0
Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models—0
CASP: Compression of Large Multimodal Models Based on Attention SparsityCode0
TinyR1-32B-Preview: Boosting Accuracy with Branch-Merge Distillation—0
LVLM-Compress-Bench: Benchmarking the Broader Impact of Large Vision-Language Model CompressionCode0
10K is Enough: An Ultra-Lightweight Binarized Network for Infrared Small-Target Detection—0
Beyond the Tip of Efficiency: Uncovering the Submerged Threats of Jailbreak Attacks in Small Language Models—0
Vision Transformers on the Edge: A Comprehensive Survey of Model Compression and Acceleration Strategies—0
AfroXLMR-Comet: Multilingual Knowledge Distillation with Attention Matching for Low-Resource languages—0
The Lottery LLM Hypothesis, Rethinking What Abilities Should LLM Compression Preserve?—0
Swallowing the Poison Pills: Insights from Vulnerability Disparity Among LLMs—0
When Compression Meets Model Compression: Memory-Efficient Double Compression for Large Language Models—0
Optimizing Singular Spectrum for Large Language Model Compression—0
Efficient AI in Practice: Training and Deployment of Efficient LLMs for Industry Applications—0
Vision Foundation Models in Medical Image Analysis: Advances and Challenges—0
MaskPrune: Mask-based LLM Pruning for Layer-wise Uniform Structures—0
Every Expert Matters: Towards Effective Knowledge Distillation for Mixture-of-Experts Language Models—0
OPTISHEAR: Towards Efficient and Adaptive Pruning of Large Language Models via Evolutionary Optimization—0
Vision-Language Models for Edge Networks: A Comprehensive Survey—0
Runtime Tunable Tsetlin Machines for Edge Inference on eFPGAs—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