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 801–825 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
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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