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 451–475 of 1356 papers

TitleStatusHype
Training dynamic models using early exits for automatic speech recognition on resource-constrained devicesCode0
Pruning Large Language Models via Accuracy Predictor—0
Two-Step Knowledge Distillation for Tiny Speech Enhancement—0
CoLLD: Contrastive Layer-to-layer Distillation for Compressing Multilingual Pre-trained Speech Encoders—0
Training Acceleration of Low-Rank Decomposed Networks using Sequential Freezing and Rank Quantization—0
Norm Tweaking: High-performance Low-bit Quantization of Large Language Models—0
Compressing Vision Transformers for Low-Resource Visual LearningCode0
ADC/DAC-Free Analog Acceleration of Deep Neural Networks with Frequency Transformation—0
Uncovering the Hidden Cost of Model CompressionCode0
Computation-efficient Deep Learning for Computer Vision: A Survey—0
Improving Knowledge Distillation for BERT Models: Loss Functions, Mapping Methods, and Weight Tuning—0
OmniQuant: Omnidirectionally Calibrated Quantization for Large Language ModelsCode2
DLIP: Distilling Language-Image Pre-training—0
QD-BEV : Quantization-aware View-guided Distillation for Multi-view 3D Object Detection—0
Learning Disentangled Representation with Mutual Information Maximization for Real-Time UAV Tracking—0
An Empirical Study of CLIP for Text-based Person SearchCode1
SHARK: A Lightweight Model Compression Approach for Large-scale Recommender Systems—0
Diffusion Models for Image Restoration and Enhancement -- A Comprehensive SurveyCode2
Spike-and-slab shrinkage priors for structurally sparse Bayesian neural networks—0
Benchmarking Adversarial Robustness of Compressed Deep Learning Models—0
Shortcut-V2V: Compression Framework for Video-to-Video Translation based on Temporal Redundancy Reduction—0
A Survey on Model Compression for Large Language Models—0
FedEdge AI-TC: A Semi-supervised Traffic Classification Method based on Trusted Federated Deep Learning for Mobile Edge Computing—0
Resource Constrained Model Compression via Minimax Optimization for Spiking Neural NetworksCode0
Accurate Retraining-free Pruning for Pretrained Encoder-based Language ModelsCode1
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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