Artificial Intelligence Applications in Engineering (MUH-920), week 11 of 14: interactive lab
Prof. Dr. Utku Kose, Süleyman Demirel University
Part A lets a greyscale image be drawn or generated, applies a chosen or hand-edited 3 by 3 filter, and shows the feature map, its ReLU and a 2 by 2 max pooling, which are the three steps of a convolutional block [1]. Part B computes output sizes, parameter counts and receptive fields for a stack of convolution and pooling layers.
Six questions with instant feedback. Rate your confidence before checking each answer.
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After the export, continue with the discipline challenge and the weekly task in the week overview.
[1] LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. https://doi.org/10.1109/5.726791
[2] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 770-778). https://doi.org/10.1109/CVPR.2016.90
[3] Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention (MICCAI 2015), LNCS 9351 (pp. 234-241). Springer. https://doi.org/10.1007/978-3-319-24574-4_28
[4] Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual explanations from deep networks via gradient-based localization. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 618-626). https://doi.org/10.1109/ICCV.2017.74