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 976–1000 of 1356 papers

TitleStatusHype
LPRNet: Lightweight Deep Network by Low-rank Pointwise Residual Convolution—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
MARS: Multi-macro Architecture SRAM CIM-Based Accelerator with Co-designed Compressed Neural Networks—0
MaskPrune: Mask-based LLM Pruning for Layer-wise Uniform Structures—0
Match to Win: Analysing Sequences Lengths for Efficient Self-supervised Learning in Speech and Audio—0
Matrix and tensor decompositions for training binary neural networks—0
Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons—0
Against Membership Inference Attack: Pruning is All You Need—0
MCNC: Manifold Constrained Network Compression—0
Robust Membership Encoding: Inference Attacks and Copyright Protection for Deep Learning—0
Memory- and Communication-Aware Model Compression for Distributed Deep Learning Inference on IoT—0
Memory-Efficient Vision Transformers: An Activation-Aware Mixed-Rank Compression Strategy—0
Memory-Friendly Scalable Super-Resolution via Rewinding Lottery Ticket Hypothesis—0
Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains—0
MICIK: MIning Cross-Layer Inherent Similarity Knowledge for Deep Model Compression—0
MIMONet: Multi-Input Multi-Output On-Device Deep Learning—0
MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Tasks—0
Minimally Invasive Surgery for Sparse Neural Networks in Contrastive Manner—0
Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal—0
Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework—0
MLKD-BERT: Multi-level Knowledge Distillation for Pre-trained Language Models—0
MLPrune: Multi-Layer Pruning for Automated Neural Network Compression—0
MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases—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