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 1276–1300 of 1356 papers

TitleStatusHype
Cascaded channel pruning using hierarchical self-distillation—0
Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing—0
Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey—0
Developing Far-Field Speaker System Via Teacher-Student Learning—0
Differentiable Architecture Compression—0
Differentiable Feature Aggregation Search for Knowledge Distillation—0
Differentiable Mask for Pruning Convolutional and Recurrent Networks—0
Can We Find Strong Lottery Tickets in Generative Models?—0
Differentiable Network Pruning for Microcontrollers—0
Differentiable Sparsification for Deep Neural Networks—0
Differentiable Sparsification for Deep Neural Networks—0
Structured Compression by Weight Encryption for Unstructured Pruning and Quantization—0
Differentially Private Model Compression—0
Differential Privacy Meets Federated Learning under Communication Constraints—0
Can Students Outperform Teachers in Knowledge Distillation based Model Compression?—0
Dimensionality Reduced Training by Pruning and Freezing Parts of a Deep Neural Network, a Survey—0
DiPaCo: Distributed Path Composition—0
DipSVD: Dual-importance Protected SVD for Efficient LLM Compression—0
Can Students Beyond The Teacher? Distilling Knowledge from Teacher's Bias—0
Discrete Model Compression With Resource Constraint for Deep Neural Networks—0
Can Model Compression Improve NLP Fairness—0
Can collaborative learning be private, robust and scalable?—0
CAIT: Triple-Win Compression towards High Accuracy, Fast Inference, and Favorable Transferability For ViTs—0
DistilDoc: Knowledge Distillation for Visually-Rich Document Applications—0
Structured Convolutions for Efficient Neural Network Design—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