Image Classification On Fashion Mnist

Accuracy

Results

Performance results of various models on this benchmark

Comparison Table

Model Name	Accuracy
tmcomposites-plug-and-play-collaboration	93.0
textcaps-handwritten-character-recognition	-
understanding-and-enhancing-mixed-sample-data	-
the-convolutional-tsetlin-machine	91.5
the-convolutional-tsetlin-machine	88.25
accelerating-spiking-neural-network-training	89.05
cnn-filter-db-an-empirical-investigation-of	94.44
random-erasing-data-augmentation	-
vision-eagle-attention-a-new-lens-for	93.30
towards-physical-plausibility-in	0.902
training-neural-networks-with-local-error	-
preventing-manifold-intrusion-with-locality	-
spike-time-displacement-based-error	92.8
online-training-through-time-for-spiking	-
wavemix-lite-a-resource-efficient-neural	-
sparse-spiking-gradient-descent	82.7
star-algorithm-for-nn-ensembling	92.3
parametric-matrix-models	88.58
sparse-spiking-gradient-descent	86.7
rethinking-recurrent-neural-networks-and	-
neupde-neural-network-based-ordinary-and	-
Model 22	91.60
a-block-based-convolutional-neural-network	95.03
learning-local-discrete-features-in	93.45
a-single-graph-convolution-is-all-you-need	88.09
improving-k-means-clustering-performance-with	84.4
learning-in-wilson-cowan-model-for	91.35
learning-in-wilson-cowan-model-for	88.39
towards-physical-plausibility-in	0.904
real-valued-continued-fraction-of-straight	84.12
accelerating-spiking-neural-network-training	90.57
personalized-federated-learning-with-hidden	99.06
parametric-matrix-models	90.89
vision-eagle-attention-a-new-lens-for	92.28