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 951–975 of 1356 papers

TitleStatusHype
Enabling Retrain-free Deep Neural Network Pruning using Surrogate Lagrangian Relaxation—0
Computation-Efficient Knowledge Distillation via Uncertainty-Aware MixupCode1
Neural Pruning via Growing RegularizationCode1
Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks—0
Distilling Optimal Neural Networks: Rapid Search in Diverse Spaces—0
Wasserstein Contrastive Representation Distillation—0
Reinforced Multi-Teacher Selection for Knowledge Distillation—0
Large-Scale Generative Data-Free Distillation—0
Inferring ECG from PPG for Continuous Cardiac Monitoring Using Lightweight Neural Network—0
Progressive Network Grafting for Few-Shot Knowledge DistillationCode1
Mix and Match: A Novel FPGA-Centric Deep Neural Network Quantization Framework—0
DE-RRD: A Knowledge Distillation Framework for Recommender SystemCode1
Model Compression Using Optimal Transport—0
Multi-head Knowledge Distillation for Model Compression—0
Going Beyond Classification Accuracy Metrics in Model CompressionCode1
Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains—0
Compressing Pre-trained Language Models by Matrix Decomposition—0
Edge Deep Learning for Neural Implants—0
Multi-level Knowledge Distillation via Knowledge Alignment and CorrelationCode1
NPAS: A Compiler-aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration—0
Self-Supervised Generative Adversarial Compression—0
Reverse-engineering recurrent neural network solutions to a hierarchical inference task for mice—0
A Selective Survey on Versatile Knowledge Distillation Paradigm for Neural Network Models—0
Extreme Model Compression for On-device Natural Language Understanding—0
KD-Lib: A PyTorch library for Knowledge Distillation, Pruning and QuantizationCode1
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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