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 1326–1350 of 1356 papers

TitleStatusHype
Dreaming To Prune Image Deraining Networks—0
Stochastic Model Pruning via Weight Dropping Away and Back—0
Bridging the Gap Between Foundation Models and Heterogeneous Federated Learning—0
Dual Discriminator Adversarial Distillation for Data-free Model Compression—0
Boosting Graph Neural Networks via Adaptive Knowledge Distillation—0
Dual sparse training framework: inducing activation map sparsity via Transformed 1 regularization—0
Structured Pruning for Multi-Task Deep Neural Networks—0
Dynamically Hierarchy Revolution: DirNet for Compressing Recurrent Neural Network on Mobile Devices—0
Block-wise Intermediate Representation Training for Model Compression—0
Block Skim Transformer for Efficient Question Answering—0
Dynamic Model Pruning with Feedback—0
Dynamic Probabilistic Pruning: Training sparse networks based on stochastic and dynamic masking—0
Structured Pruning is All You Need for Pruning CNNs at Initialization—0
Blending LSTMs into CNNs—0
Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation—0
DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization—0
Structured Pruning Learns Compact and Accurate Models—0
BioNetExplorer: Architecture-Space Exploration of Bio-Signal Processing Deep Neural Networks for Wearables—0
ECoFLaP: Efficient Coarse-to-Fine Layer-Wise Pruning for Vision-Language Models—0
EDCompress: Energy-Aware Model Compression for Dataflows—0
Edge AI: Evaluation of Model Compression Techniques for Convolutional Neural Networks—0
Edge-AI for Agriculture: Lightweight Vision Models for Disease Detection in Resource-Limited Settings—0
Edge Deep Learning for Neural Implants—0
Edge-First Language Model Inference: Models, Metrics, and Tradeoffs—0
Edge-MultiAI: Multi-Tenancy of Latency-Sensitive Deep Learning Applications on Edge—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