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 601–625 of 1356 papers

TitleStatusHype
An Efficient Method of Training Small Models for Regression Problems with Knowledge Distillation—0
Distilling Inductive Bias: Knowledge Distillation Beyond Model Compression—0
BinaryBERT: Pushing the Limit of BERT Quantization—0
An Effective Information Theoretic Framework for Channel Pruning—0
AdaKD: Dynamic Knowledge Distillation of ASR models using Adaptive Loss Weighting—0
Accelerating Deep Learning with Dynamic Data Pruning—0
DopQ-ViT: Towards Distribution-Friendly and Outlier-Aware Post-Training Quantization for Vision Transformers—0
2-bit Model Compression of Deep Convolutional Neural Network on ASIC Engine for Image Retrieval—0
DistilDoc: Knowledge Distillation for Visually-Rich Document Applications—0
Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures—0
An Automatic and Efficient BERT Pruning for Edge AI Systems—0
Discrete Model Compression With Resource Constraint for Deep Neural Networks—0
Beyond the Tip of Efficiency: Uncovering the Submerged Threats of Jailbreak Attacks in Small Language Models—0
DipSVD: Dual-importance Protected SVD for Efficient LLM Compression—0
DiPaCo: Distributed Path Composition—0
Analysis of Quantization on MLP-based Vision Models—0
AdaDeep: A Usage-Driven, Automated Deep Model Compression Framework for Enabling Ubiquitous Intelligent Mobiles—0
Dimensionality Reduced Training by Pruning and Freezing Parts of a Deep Neural Network, a Survey—0
Beware of Calibration Data for Pruning Large Language Models—0
Differential Privacy Meets Federated Learning under Communication Constraints—0
Differentially Private Model Compression—0
Analysis of memory consumption by neural networks based on hyperparameters—0
Differentiable Sparsification for Deep Neural Networks—0
Differentiable Sparsification for Deep Neural Networks—0
Differentiable Network Pruning for Microcontrollers—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