Image Classification On Fashion Mnist
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
Modellname | 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 | - |
Modell 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 |