Image Classification On Fashion Mnist

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name	Accuracy	Paper Title	Repository
Tsetlin Machine Composites	93.0	TMComposites: Plug-and-Play Collaboration Between Specialized Tsetlin Machines	-
TextCaps	-	TextCaps : Handwritten Character Recognition with Very Small Datasets	-
PreAct-ResNet18 + FMix	-	FMix: Enhancing Mixed Sample Data Augmentation	-
CTM-8000 (Convolutional Tsetlin Machine)	91.5	The Convolutional Tsetlin Machine	-
CTM-250 (Convolutional Tsetlin Machine)	88.25	The Convolutional Tsetlin Machine	-
FastSNN (MLP)	89.05	Robust and accelerated single-spike spiking neural network training with applicability to challenging temporal tasks	-
Inception v3	94.44	CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters	-
Random Erasing	-	Random Erasing Data Augmentation	-
ResNet-18 + Vision Eagle Attention	93.30	Vision Eagle Attention: a new lens for advancing image classification	-
ENERGIZE	0.902	Towards Physical Plausibility in Neuroevolution Systems	-
VGG8B(2x) + LocalLearning + CO	-	Training Neural Networks with Local Error Signals	-
Local Mixup DenseNet	-	Preventing Manifold Intrusion with Locality: Local Mixup	-
StiDi-BP in R-CSNN	92.8	Spike time displacement based error backpropagation in convolutional spiking neural networks	-
OTTT	-	Online Training Through Time for Spiking Neural Networks	-
WaveMixLite	-	WaveMix: A Resource-efficient Neural Network for Image Analysis	-
Sparse Spiking Gradient Descent (MLP)	82.7	Sparse Spiking Gradient Descent	-
Star Algorithm on LeNet	92.3	Star algorithm for NN ensembling	-
PMM (Parametric Matrix Model)	88.58	Parametric Matrix Models	-
Sparse Spiking Gradient Descent (CNN)	86.7	Sparse Spiking Gradient Descent	-
E2E-3M	-	Rethinking Recurrent Neural Networks and Other Improvements for Image Classification	-

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