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 801–850 of 1356 papers

TitleStatusHype
HFSP: A Hardware-friendly Soft Pruning Framework for Vision Transformers—0
Bayesian Optimization with Clustering and Rollback for CNN Auto PruningCode0
Backdoor Attacks on Federated Learning with Lottery Ticket HypothesisCode1
DS-Net++: Dynamic Weight Slicing for Efficient Inference in CNNs and TransformersCode1
Distilling Linguistic Context for Language Model CompressionCode1
Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications—0
The NiuTrans System for WNGT 2020 Efficiency TaskCode1
Experimental implementation of a neural network optical channel equalizer in restricted hardware using pruning and quantization—0
Multihop: Leveraging Complex Models to Learn Accurate Simple Models—0
A Note on Knowledge Distillation Loss Function for Object Classification—0
Causal Explanation of Convolutional Neural NetworksCode0
KroneckerBERT: Learning Kronecker Decomposition for Pre-trained Language Models via Knowledge Distillation—0
How to Select One Among All? An Extensive Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language UnderstandingCode1
BioNetExplorer: Architecture-Space Exploration of Bio-Signal Processing Deep Neural Networks for Wearables—0
GDP: Stabilized Neural Network Pruning via Gates with Differentiable Polarization—0
Full-Cycle Energy Consumption Benchmark for Low-Carbon Computer Vision—0
Lipschitz Continuity Guided Knowledge Distillation—0
DKM: Differentiable K-Means Clustering Layer for Neural Network Compression—0
YANMTT: Yet Another Neural Machine Translation Toolkit—0
Small Object Detection Based on Modified FSSD and Model Compression—0
An Information Theory-inspired Strategy for Automatic Network PruningCode1
Scaling Laws for Deep Learning—0
Pruning vs XNOR-Net: A Comprehensive Study of Deep Learning for Audio Classification on Edge-devicesCode0
Preventing Catastrophic Forgetting and Distribution Mismatch in Knowledge Distillation via Synthetic Data—0
Visual Domain Adaptation for Monocular Depth Estimation on Resource-Constrained HardwareCode0
Random Offset Block Embedding Array (ROBE) for CriteoTB Benchmark MLPerf DLRM Model : 1000 Compression and 3.1 Faster Inference—0
Learning a Neural Diff for Speech Models—0
QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning—0
Towards Efficient Tensor Decomposition-Based DNN Model Compression with Optimization Framework—0
Pruning Ternary Quantization—0
A New Clustering-Based Technique for the Acceleration of Deep Convolutional Networks—0
Accelerating deep neural networks for efficient scene understanding in automotive cyber-physical systems—0
Federated Action Recognition on Heterogeneous Embedded Devices—0
Efficient automated U-Net based tree crown delineation using UAV multi-spectral imagery on embedded devices—0
Compact and Optimal Deep Learning with Recurrent Parameter GeneratorsCode0
Model compression as constrained optimization, with application to neural nets. Part V: combining compressions—0
WeClick: Weakly-Supervised Video Semantic Segmentation with Click Annotations—0
A Light-weight Deep Human Activity Recognition Algorithm Using Multi-knowledge Distillation—0
Universal approximation and model compression for radial neural networksCode0
Investigation of Practical Aspects of Single Channel Speech Separation for ASR—0
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification—0
Learning Efficient Vision Transformers via Fine-Grained Manifold DistillationCode1
Pool of Experts: Realtime Querying Specialized Knowledge in Massive Neural NetworksCode0
Exact Backpropagation in Binary Weighted Networks with Group Weight TransformationsCode0
Scalable Teacher Forcing Network for Semi-Supervised Large Scale Data Streams—0
Image Classification with CondenseNeXt for ARM-Based Computing PlatformsCode0
PQK: Model Compression via Pruning, Quantization, and Knowledge Distillation—0
Minimally Invasive Surgery for Sparse Neural Networks in Contrastive Manner—0
Network Pruning via Performance MaximizationCode0
Learning Student Networks in the WildCode2
Show:102550
← PrevPage 17 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