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 901–925 of 1356 papers

TitleStatusHype
Kernel Modulation: A Parameter-Efficient Method for Training Convolutional Neural Networks—0
KIMERA: Injecting Domain Knowledge into Vacant Transformer Heads—0
KMIR: A Benchmark for Evaluating Knowledge Memorization, Identification and Reasoning Abilities of Language Models—0
Knowledge Distillation: A Survey—0
Knowledge Distillation Based Semantic Communications For Multiple Users—0
Knowledge Distillation Beyond Model Compression—0
Knowledge Distillation for Image Restoration : Simultaneous Learning from Degraded and Clean Images—0
Knowledge Distillation for Object Detection via Rank Mimicking and Prediction-guided Feature Imitation—0
Knowledge Distillation for Object Detection: from generic to remote sensing datasets—0
Knowledge Distillation for Oriented Object Detection on Aerial Images—0
Knowledge Distillation for Swedish NER models: A Search for Performance and Efficiency—0
Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions—0
Knowledge Distillation in Vision Transformers: A Critical Review—0
Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher—0
Knowledge Distillation on Graphs: A Survey—0
Knowledge distillation via adaptive instance normalization—0
Knowledge distillation via softmax regression representation learning—0
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution—0
Representative Teacher Keys for Knowledge Distillation Model Compression Based on Attention Mechanism for Image Classification—0
Know What You Don't Need: Single-Shot Meta-Pruning for Attention Heads—0
KroneckerBERT: Learning Kronecker Decomposition for Pre-trained Language Models via Knowledge Distillation—0
KroneckerBERT: Significant Compression of Pre-trained Language Models Through Kronecker Decomposition and Knowledge Distillation—0
Kronecker Decomposition for GPT Compression—0
L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models—0
LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression—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