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tokens: Convolutional neural networks take advantage of the typically high correlation of local groups (e.g., adjacent pixels in an image) and hierarchically extract features (edges → motifs → parts → objects; LeCun et al. 2015 ). With this, deep CNNs extract, by layer-wise transformations, the feature representation of an image that serves as a basis to, for example, classify images ( Armstrong Fletcher 2019 ), predict sequences ( Upendran et al. 2020 ), transform images to different domains ( Kim et al. 2019 ; Jarolim et al. 2020 ), or provide segmentation maps ( Long et al. 2015 ). From deep learning applications for semantic segmentation, the concept of multi-scale architectures has proven to be most successful ( Ronneberger et al. 2015 ; Chen et al. 2018 ; Badrinarayanan et al. 2017 ). Here the spatial dimensions are successively reduced, while the depth of the network (number of filters) is being increased. With this, the neural network can correlate features of different scales at each resolution level and can solve more complex tasks with the increased number of filters (greater number of parameters). In order to correlate features across the full image, a sufficient network depth is required. For semantic segmentation, the output is a pixel-wise classification of the input image (segmentation map). Therefore, the feature representation (low spatial resolution) needs to be matched to the size of the input image. The segmentation map can be directly obtained from the deepest layer of the neural network (e.g., Long et al. 2015 ), but with this approach the output typically lacks spatial detail. An alternative approach, which accounts for global relations and spatial resolution simultaneously, is the use of an encoder-decoder architecture ( Ronneberger et al. 2015 ; Chen et al. 2018 ; Badrinarayanan et al. 2017 ). Here, the image is first transferred into a feature representation over multiple scales by the encoder, and then upsampled to the original resolution over the same number of scales by the decoder. At each scale, skip connections are used to combine the upsampled features, which comprise the full context information, with the spatial details from the encoder. A common problem with deep networks is the vanishing gradient problem, where the parameter optimization of the neural network becomes inefficient with increasing network depth ( He et al. 2016 ; Szegedy et al. 2015 ). In addition, when dealing with high resolutions, the training time increases exponentially, which often results in long convergence times for model training. A possible solution to this problem was developed by Karras et al. (2017) for the application of image generation. Here the authors used an approach where the size of the network is progressively increased. The advantage of this method is that the training with low-resolution samples is computationally efficient and allows the deepest layers to be trained until full convergence is reached, before introducing