Image Classification On Fashion Mnist

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Accuracy

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Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

		Paper Title	Repository
pFedBreD_ns_mg	99.06	Personalized Federated Learning with Hidden Information on Personalized Prior	-
LR-Net	95.03	LR-Net: A Block-based Convolutional Neural Network for Low-Resolution Image Classification
Inception v3	94.44	CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters
R-ExplaiNet-26	93.45	Learning local discrete features in explainable-by-design convolutional neural networks
ResNet-18 + Vision Eagle Attention	93.30	Vision Eagle Attention: a new lens for advancing image classification
Tsetlin Machine Composites	93.0	TMComposites: Plug-and-Play Collaboration Between Specialized Tsetlin Machines
StiDi-BP in R-CSNN	92.8	Spike time displacement based error backpropagation in convolutional spiking neural networks	-
Star Algorithm on LeNet	92.3	Star algorithm for NN ensembling
ResNet-18	92.28	Vision Eagle Attention: a new lens for advancing image classification
SpeedyLiteVision Network	91.60	-	-
CTM-8000 (Convolutional Tsetlin Machine)	91.5	The Convolutional Tsetlin Machine
CNN+ Wilson-Cowan model RNN	91.35	Learning in Wilson-Cowan model for metapopulation
Convolutional PMM (Parametric Matrix Model)	90.89	Parametric Matrix Models	-
FastSNN (CNN)	90.57	Robust and accelerated single-spike spiking neural network training with applicability to challenging temporal tasks
FastSNN (MLP)	89.05	Robust and accelerated single-spike spiking neural network training with applicability to challenging temporal tasks
PMM (Parametric Matrix Model)	88.58	Parametric Matrix Models	-
Wilson-Cowan model RNN	88.39	Learning in Wilson-Cowan model for metapopulation
CTM-250 (Convolutional Tsetlin Machine)	88.25	The Convolutional Tsetlin Machine
GECCO	88.09	A Single Graph Convolution Is All You Need: Efficient Grayscale Image Classification
Sparse Spiking Gradient Descent (CNN)	86.7	Sparse Spiking Gradient Descent

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Image Classification On Fashion Mnist

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