Fig 1 - uploaded by Paweł Ksieniewicz
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(Left) the schema of a block with no conv on skip connections -block A of ResNet. (Right) the schema of a block with conv on skip connection -block B of ResNet.

(Left) the schema of a block with no conv on skip connections -block A of ResNet. (Right) the schema of a block with conv on skip connection -block B of ResNet.

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In this work we explored capabilities of improving deep learning models performance by reducing the dataset imbalance. For our experiments a highly imbalanced ECG dataset MIT-BIH was used. Multiple approaches were considered. First we introduced mutliclass UMCE, the ensemble designed to deal with imbalanced datasets. Secondly, we studied the impact...

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... of the network can be obtained for each of the methods separately, but the sole purpose of this study is to compare an impact of the imbalance reduction methods, not to obtain the highest possible value of metrics. Base Model. As a base model, a ResNet with 1D convolution was utilised. Used ResNet consists of two kinds of blocks: A and B (see Fig. 1). Block B is used when the number of filters is increased. In that case convolution is applied to the skip connection. This changes the tensor shape of a skip connection (its depth) and enables adding it to an output of the two convolutional layers with an increased number of ...

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