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 226–250 of 1356 papers

TitleStatusHype
Localize-and-Stitch: Efficient Model Merging via Sparse Task ArithmeticCode1
A Web-Based Solution for Federated Learning with LLM-Based Automation—0
A Survey on Drowsiness Detection -- Modern Applications and Methods—0
Pruning By Explaining Revisited: Optimizing Attribution Methods to Prune CNNs and TransformersCode1
NeR-VCP: A Video Content Protection Method Based on Implicit Neural Representation—0
Fine-Tuning and Deploying Large Language Models Over Edges: Issues and Approaches—0
MoDeGPT: Modular Decomposition for Large Language Model Compression—0
RepControlNet: ControlNet Reparameterization—0
ABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language ModelsCode3
Computer Vision Model Compression Techniques for Embedded Systems: A SurveyCode0
An Effective Information Theoretic Framework for Channel Pruning—0
Infra-YOLO: Efficient Neural Network Structure with Model Compression for Real-Time Infrared Small Object Detection—0
Knowledge Distillation with Refined LogitsCode1
Compact 3D Gaussian Splatting for Static and Dynamic Radiance FieldsCode3
AdapMTL: Adaptive Pruning Framework for Multitask Learning Model—0
DopQ-ViT: Towards Distribution-Friendly and Outlier-Aware Post-Training Quantization for Vision Transformers—0
Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments—0
Comb, Prune, Distill: Towards Unified Pruning for Vision Model CompressionCode0
Artificial Neural Networks for Photonic Applications: From Algorithms to Implementation—0
An Efficient Real-Time Object Detection Framework on Resource-Constricted Hardware Devices via Software and Hardware Co-design—0
Tensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMs—0
NeuSemSlice: Towards Effective DNN Model Maintenance via Neuron-level Semantic Slicing—0
Generalizing Teacher Networks for Effective Knowledge Distillation Across Student ArchitecturesCode0
Comprehensive Study on Performance Evaluation and Optimization of Model Compression: Bridging Traditional Deep Learning and Large Language Models—0
LORTSAR: Low-Rank Transformer for Skeleton-based Action Recognition—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