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

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Showing 101–150 of 1356 papers

TitleStatusHype
Synergistic Effects of Knowledge Distillation and Structured Pruning for Self-Supervised Speech Models—0
Theoretical Guarantees for Low-Rank Compression of Deep Neural Networks—0
Activation-Informed Merging of Large Language ModelsCode1
Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity—0
MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Tasks—0
Attention Sinks and Outlier Features: A 'Catch, Tag, and Release' Mechanism for Embeddings—0
Role of Mixup in Topological Persistence Based Knowledge Distillation for Wearable Sensor Data—0
Huff-LLM: End-to-End Lossless Compression for Efficient LLM Inference—0
Efficient Supernet Training with Orthogonal Softmax for Scalable ASR Model Compression—0
Pivoting Factorization: A Compact Meta Low-Rank Representation of Sparsity for Efficient Inference in Large Language Models—0
SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion TransformerCode9
Perforated Backpropagation: A Neuroscience Inspired Extension to Artificial Neural NetworksCode0
TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Models—0
You Only Prune Once: Designing Calibration-Free Model Compression With Policy Learning—0
On Accelerating Edge AI: Optimizing Resource-Constrained Environments—0
SwiftPrune: Hessian-Free Weight Pruning for Large Language Models—0
Practical quantum federated learning and its experimental demonstration—0
MultiPruner: Balanced Structure Removal in Foundation ModelsCode0
Knowledge Distillation for Image Restoration : Simultaneous Learning from Degraded and Clean Images—0
Atleus: Accelerating Transformers on the Edge Enabled by 3D Heterogeneous Manycore Architectures—0
FASP: Fast and Accurate Structured Pruning of Large Language Models—0
SWSC: Shared Weight for Similar Channel in LLM—0
A Survey on Dynamic Neural Networks: from Computer Vision to Multi-modal Sensor FusionCode1
Tensorization of neural networks for improved privacy and interpretabilityCode0
Merging Feed-Forward Sublayers for Compressed TransformersCode1
Neural Architecture Codesign for Fast Physics ApplicationsCode0
UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles—0
CURing Large Models: Compression via CUR Decomposition—0
Effective and Efficient Mixed Precision Quantization of Speech Foundation Models—0
LightGNN: Simple Graph Neural Network for RecommendationCode2
Strategic Fusion Optimizes Transformer Compression—0
Optimizing Small Language Models for In-Vehicle Function-Calling—0
DeepCompress-ViT: Rethinking Model Compression to Enhance Efficiency of Vision Transformers at the EdgeCode0
Once-Tuning-Multiple-Variants: Tuning Once and Expanded as Multiple Vision-Language Model Variants—0
CoA: Towards Real Image Dehazing via Compression-and-AdaptationCode1
Random Conditioning for Diffusion Model Compression with Distillation—0
Improving Acoustic Scene Classification in Low-Resource Conditions—0
Feature Alignment-Based Knowledge Distillation for Efficient Compression of Large Language Models—0
Optimization and Scalability of Collaborative Filtering Algorithms in Large Language Models—0
HTR-JAND: Handwritten Text Recognition with Joint Attention Network and Knowledge DistillationCode0
Edge-AI for Agriculture: Lightweight Vision Models for Disease Detection in Resource-Limited Settings—0
GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference—0
Singular Value Scaling: Efficient Generative Model Compression via Pruned Weights RefinementCode0
CoSurfGS:Collaborative 3D Surface Gaussian Splatting with Distributed Learning for Large Scene Reconstruction—0
Lightweight Design and Optimization methods for DCNNs: Progress and Futures—0
Semantics Prompting Data-Free Quantization for Low-Bit Vision Transformers—0
Deploying Foundation Model Powered Agent Services: A Survey—0
Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LNCode1
RemoteTrimmer: Adaptive Structural Pruning for Remote Sensing Image ClassificationCode0
TrimLLM: Progressive Layer Dropping for Domain-Specific LLMs—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