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 601–650 of 1356 papers

TitleStatusHype
Greener yet Powerful: Taming Large Code Generation Models with Quantization—0
Group channel pruning and spatial attention distilling for object detection—0
GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking—0
Atrial Fibrillation Detection Using Weight-Pruned, Log-Quantised Convolutional Neural Networks—0
Conditional Automated Channel Pruning for Deep Neural Networks—0
HadaNets: Flexible Quantization Strategies for Neural Networks—0
HALOC: Hardware-Aware Automatic Low-Rank Compression for Compact Neural Networks—0
A flexible, extensible software framework for model compression based on the LC algorithm—0
HCE: Improving Performance and Efficiency with Heterogeneously Compressed Neural Network Ensemble—0
Investigation of Practical Aspects of Single Channel Speech Separation for ASR—0
HFSP: A Hardware-friendly Soft Pruning Framework for Vision Transformers—0
HideNseek: Federated Lottery Ticket via Server-side Pruning and Sign Supermask—0
Cross-Channel Intragroup Sparsity Neural Network—0
Attention Sinks and Outlier Features: A 'Catch, Tag, and Release' Mechanism for Embeddings—0
ConaCLIP: Exploring Distillation of Fully-Connected Knowledge Interaction Graph for Lightweight Text-Image Retrieval—0
HODEC: Towards Efficient High-Order DEcomposed Convolutional Neural Networks—0
Formalizing Generalization and Robustness of Neural Networks to Weight Perturbations—0
How and When Adversarial Robustness Transfers in Knowledge Distillation?—0
Aerial Image Classification in Scarce and Unconstrained Environments via Conformal Prediction—0
Deep Face Recognition Model Compression via Knowledge Transfer and Distillation—0
How to Explain Neural Networks: an Approximation Perspective—0
How to Select One Among All ? An Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language Understanding—0
Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations—0
Redundancy and Concept Analysis for Code-trained Language Models—0
CURing Large Models: Compression via CUR Decomposition—0
Huff-LLM: End-to-End Lossless Compression for Efficient LLM Inference—0
SwiftPrune: Hessian-Free Weight Pruning for Large Language Models—0
D^2MoE: Dual Routing and Dynamic Scheduling for Efficient On-Device MoE-based LLM Serving—0
FoldGPT: Simple and Effective Large Language Model Compression Scheme—0
DARB: A Density-Aware Regular-Block Pruning for Deep Neural Networks—0
ICD-Face: Intra-class Compactness Distillation for Face Recognition—0
Identifying Sub-networks in Neural Networks via Functionally Similar Representations—0
ILMPQ : An Intra-Layer Multi-Precision Deep Neural Network Quantization framework for FPGA—0
DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer—0
FLOPs as a Direct Optimization Objective for Learning Sparse Neural Networks—0
Impact of Disentanglement on Pruning Neural Networks—0
Implicit Neural Representation for Videos Based on Residual Connection—0
A Survey on Drowsiness Detection -- Modern Applications and Methods—0
Computation-efficient Deep Learning for Computer Vision: A Survey—0
A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification and Domain Adaptation—0
Improve Knowledge Distillation via Label Revision and Data Selection—0
Interpreting Deep Classifier by Visual Distillation of Dark Knowledge—0
Intrinsically Sparse Long Short-Term Memory Networks—0
Improving Knowledge Distillation for BERT Models: Loss Functions, Mapping Methods, and Weight Tuning—0
Is Quantum Optimization Ready? An Effort Towards Neural Network Compression using Adiabatic Quantum Computing—0
FlatENN: Train Flat for Enhanced Fault Tolerance of Quantized Deep Neural Networks—0
FIT: A Metric for Model Sensitivity—0
In defense of parameter sharing for model-compression—0
Individual Content and Motion Dynamics Preserved Pruning for Video Diffusion Models—0
Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead—0
Show:102550
← PrevPage 13 of 28Next →
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