Dear Colleagues, 

The outputs of deep neural networks usually have different abstraction levels from the input images, if trained with proper data and appropriate learning methods. For example, in the case of convolutional neural networks with smaller kernel sizes, in most cases it is known that the first few layer outputs are simple edge and corner detectors, that outputs from middle layers often react to simple parts of the visual objects, and that outputs from the layers close to the target labels model the most abstractive information of the visual objects. 

This gives us the possibility of using intermediate layer outputs as multi-level descriptors for the given visual objects, which may be used for some other purposes such as explaining the prediction from the networks, reconstruction of the input images, style transfer, etc. Another usage of intermediate layers is the re-use of the information from the previous layers not to lose input information. ResNet and U-Net are recent deep learning models for re-using the outputs from the previous layers. 

This Special Issue focuses on how multiple-level visual features are used. The topics of interest include, but are not limited to: 

• Shortcut modification in residual networks and U-Net
• Feature extraction for semantic segmentation
• Nonlinear feature transformation using deep learning
• Multiple-level output fusion in deep neural networks
• Multiple-level feature transformation in deep neural networks
• Weakly supervised learning visual feature extraction
• Pooling methods for feature abstraction in deep neural networks
• Nonlinear dimensionality reduction by deep neural networks

Prof. Dr. Gil-Jin Jang
Guest Editor

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• Shortcut modification in residual networks and U-Net
• Feature extraction for semantic segmentation
• Nonlinear feature transformation using deep learning
• Multiple-level output fusion in deep neural networks
• Multiple-level feature transformation in deep neural networks
• Weakly supervised learning visual feature extraction
• Pooling methods for feature abstraction in deep neural networks
• Nonlinear dimensionality reduction by deep neural networks.