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 851–875 of 1356 papers

TitleStatusHype
Unraveling Key Factors of Knowledge Distillation—0
Unsupervised model compression for multilayer bootstrap networks—0
UPAQ: A Framework for Real-Time and Energy-Efficient 3D Object Detection in Autonomous Vehicles—0
USDC: Unified Static and Dynamic Compression for Visual Transformer—0
USM-Lite: Quantization and Sparsity Aware Fine-tuning for Speech Recognition with Universal Speech Models—0
Value-Based Deep Multi-Agent Reinforcement Learning with Dynamic Sparse Training—0
Variational autoencoder-based neural network model compression—0
VIC-KD: Variance-Invariance-Covariance Knowledge Distillation to Make Keyword Spotting More Robust Against Adversarial Attacks—0
Vision Foundation Models in Medical Image Analysis: Advances and Challenges—0
Vision-Language Models for Edge Networks: A Comprehensive Survey—0
Vision Transformers on the Edge: A Comprehensive Survey of Model Compression and Acceleration Strategies—0
VQ4ALL: Efficient Neural Network Representation via a Universal Codebook—0
Wasserstein Contrastive Representation Distillation—0
Watermarking Graph Neural Networks by Random Graphs—0
WeClick: Weakly-Supervised Video Semantic Segmentation with Click Annotations—0
Weight, Block or Unit? Exploring Sparsity Tradeoffs for Speech Enhancement on Tiny Neural Accelerators—0
Weight Normalization based Quantization for Deep Neural Network Compression—0
Weight Squeezing: Reparameterization for Knowledge Transfer and Model Compression—0
Weight Squeezing: Reparameterization for Compression and Fast Inference—0
Weight Squeezing: Reparameterization for Knowledge Transfer and Model Compression—0
Robustness Challenges in Model Distillation and Pruning for Natural Language Understanding—0
What do larger image classifiers memorise?—0
What is Left After Distillation? How Knowledge Transfer Impacts Fairness and Bias—0
What is Lost in Knowledge Distillation?—0
What Makes a Good Dataset for Knowledge Distillation?—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