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 676–700 of 1356 papers

TitleStatusHype
QAPPA: Quantization-Aware Power, Performance, and Area Modeling of DNN Accelerators—0
Dimensionality Reduced Training by Pruning and Freezing Parts of a Deep Neural Network, a Survey—0
Perturbation of Deep Autoencoder Weights for Model Compression and Classification of Tabular Data—0
Chemical transformer compression for accelerating both training and inference of molecular modelingCode0
DNA data storage, sequencing data-carrying DNA—0
Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures—0
Data-Free Adversarial Knowledge Distillation for Graph Neural Networks—0
Automatic Block-wise Pruning with Auxiliary Gating Structures for Deep Convolutional Neural Networks—0
Online Model Compression for Federated Learning with Large Models—0
Can collaborative learning be private, robust and scalable?—0
Multi-Granularity Structural Knowledge Distillation for Language Model CompressionCode0
Towards Feature Distribution Alignment and Diversity Enhancement for Data-Free Quantization—0
Leaner and Faster: Two-Stage Model Compression for Lightweight Text-Image RetrievalCode1
Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications—0
Neural Network Pruning by Cooperative Coevolution—0
Characterizing and Understanding the Behavior of Quantized Models for Reliable DeploymentCode0
Enabling All In-Edge Deep Learning: A Literature Review—0
LilNetX: Lightweight Networks with EXtreme Model Compression and Structured SparsificationCode0
Masking Adversarial Damage: Finding Adversarial Saliency for Robust and Sparse NetworkCode1
FedSynth: Gradient Compression via Synthetic Data in Federated Learning—0
Aligned Weight Regularizers for Pruning Pretrained Neural Networks—0
Structured Pruning Learns Compact and Accurate ModelsCode1
TextPruner: A Model Pruning Toolkit for Pre-Trained Language Models—0
Kernel Modulation: A Parameter-Efficient Method for Training Convolutional Neural Networks—0
CHEX: CHannel EXploration for CNN Model CompressionCode1
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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