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 776–800 of 1356 papers

TitleStatusHype
LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation—0
An Overview of Neural Network Compression—0
Lossless Model Compression via Joint Low-Rank Factorization Optimization—0
Lottery Hypothesis based Unsupervised Pre-training for Model Compression in Federated Learning—0
Lottery Ticket Preserves Weight Correlation: Is It Desirable or Not?—0
Low-Complexity Acoustic Scene Classification Using Data Augmentation and Lightweight ResNet—0
Low-Complexity Inference in Continual Learning via Compressed Knowledge Transfer—0
Low-Rank Compression for IMC Arrays—0
Low-Rank Correction for Quantized LLMs—0
Low-Rank Matrix Approximation for Neural Network Compression—0
Low Rank Optimization for Efficient Deep Learning: Making A Balance between Compact Architecture and Fast Training—0
Low-Rank Prune-And-Factorize for Language Model Compression—0
Low-rank Tensor Decomposition for Compression of Convolutional Neural Networks Using Funnel Regularization—0
LPRNet: Lightweight Deep Network by Low-rank Pointwise Residual Convolution—0
A Novel Architecture Slimming Method for Network Pruning and Knowledge Distillation—0
TinyM^2Net-V3: Memory-Aware Compressed Multimodal Deep Neural Networks for Sustainable Edge Deployment—0
Weight, Block or Unit? Exploring Sparsity Tradeoffs for Speech Enhancement on Tiny Neural Accelerators—0
Magic for the Age of Quantized DNNs—0
Making deep neural networks work for medical audio: representation, compression and domain adaptation—0
Mamba-PTQ: Outlier Channels in Recurrent Large Language Models—0
TinyR1-32B-Preview: Boosting Accuracy with Branch-Merge Distillation—0
MARS: Multi-macro Architecture SRAM CIM-Based Accelerator with Co-designed Compressed Neural Networks—0
An Improving Framework of regularization for Network Compression—0
A New Clustering-Based Technique for the Acceleration of Deep Convolutional Networks—0
MaskPrune: Mask-based LLM Pruning for Layer-wise Uniform Structures—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