Efficient-CapsNet: Capsule Network with Self-Attention Routing

Deep convolutional neural networks, assisted by architectural design strategies, make extensive use of data augmentation techniques and layers with a high number of feature maps to embed object transformations. That is highly inefficient and for large datasets implies a massive redundancy of features detectors... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Image Classification MNIST Efficient-CapsNet Percentage error 0.16 # 1
Accuracy 99.84 # 1
Trainable Parameters 161,824 # 1
Image Classification smallNORB Efficient-CapsNet Classification Error 1.23 # 1

Methods used in the Paper


METHOD TYPE
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