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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