Competitive Multi-scale Convolution

18 Nov 2015 Zhibin Liao Gustavo Carneiro

In this paper, we introduce a new deep convolutional neural network (ConvNet) module that promotes competition among a set of multi-scale convolutional filters. This new module is inspired by the inception module, where we replace the original collaborative pooling stage (consisting of a concatenation of the multi-scale filter outputs) by a competitive pooling represented by a maxout activation unit... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Image Classification CIFAR-10 CMsC Percentage correct 93.1 # 82
Image Classification CIFAR-100 CMsC Percentage correct 72.4 # 81
Image Classification MNIST CMsC Percentage error 0.3 # 10
Image Classification SVHN CMsC Percentage error 1.8 # 17

Methods used in the Paper


METHOD TYPE
Maxout
Activation Functions