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 651–675 of 1356 papers

TitleStatusHype
PruMUX: Augmenting Data Multiplexing with Model CompressionCode0
RAND: Robustness Aware Norm Decay For Quantized Seq2seq Models—0
Selective Pre-training for Private Fine-tuningCode0
Revisiting Data Augmentation in Model Compression: An Empirical and Comprehensive Study—0
Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt—0
Towards Understanding and Improving Knowledge Distillation for Neural Machine TranslationCode0
GSB: Group Superposition Binarization for Vision Transformer with Limited Training SamplesCode0
CrAFT: Compression-Aware Fine-Tuning for Efficient Visual Task Adaptation—0
Redundancy and Concept Analysis for Code-trained Language Models—0
CORSD: Class-Oriented Relational Self Distillation—0
Guaranteed Quantization Error Computation for Neural Network Model Compression—0
Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures—0
Deep Collective Knowledge Distillation—0
Learning Accurate Performance Predictors for Ultrafast Automated Model CompressionCode0
Structured Pruning for Multi-Task Deep Neural Networks—0
Surrogate Lagrangian Relaxation: A Path To Retrain-free Deep Neural Network Pruning—0
oBERTa: Improving Sparse Transfer Learning via improved initialization, distillation, and pruning regimes—0
A Multi-objective Complex Network Pruning Framework Based on Divide-and-conquer and Global Performance Impairment Ranking—0
Information-Theoretic GAN Compression with Variational Energy-based Model—0
Tetra-AML: Automatic Machine Learning via Tensor Networks—0
Towards Accurate Post-Training Quantization for Vision Transformer—0
Low Rank Optimization for Efficient Deep Learning: Making A Balance between Compact Architecture and Fast Training—0
Exploring Turkish Speech Recognition via Hybrid CTC/Attention Architecture and Multi-feature Fusion Network—0
R2 Loss: Range Restriction Loss for Model Compression and Quantization—0
A Contrastive Knowledge Transfer Framework for Model Compression and Transfer LearningCode0
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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