Image Classification On Mnist

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Percentage error

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	Percentage error	Paper Title	Repository
MCDNN	0.23	Multi-column Deep Neural Networks for Image Classification	-
SEER (RegNet10B)	0.58	Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision	-
Second Order Neural Ordinary Differential Equation	0.37	On Second Order Behaviour in Augmented Neural ODEs	-
FLSCNN	0.4	Enhanced Image Classification With a Fast-Learning Shallow Convolutional Neural Network	-
PCANet	0.6	PCANet: A Simple Deep Learning Baseline for Image Classification?	-
CNN+ Wilson-Cowan model RNN	-	Learning in Wilson-Cowan model for metapopulation	-
BNM NiN	0.24	Batch-normalized Maxout Network in Network	-
ResNet-9	-	CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters	-
Deep Fried Convnets	0.7	Deep Fried Convnets	-
EXACT (M3-CNN)	0.33	EXACT: How to Train Your Accuracy	-
SimpleNetv1	0.25	Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures	-
NiN	0.5	Network In Network	-
Tsetlin Machine	1.8	The Tsetlin Machine -- A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic	-
VGG-5 (Spinal FC)	0.28	SpinalNet: Deep Neural Network with Gradual Input	-
StiDi-BP in R-CSNN	-	Spike time displacement based error backpropagation in convolutional spiking neural networks	-
Explaining and Harnessing Adversarial Examples	0.8	Explaining and Harnessing Adversarial Examples	-
pFedBreD_ns_mg	-	Personalized Federated Learning with Hidden Information on Personalized Prior	-
Wilson-Cowan model RNN	-	Learning in Wilson-Cowan model for metapopulation	-
RMDL (30 RDLs)	0.18	RMDL: Random Multimodel Deep Learning for Classification	-
TAAF-CNN	0.48%	Evaluating the Performance of TAAF for image classification models

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