How does the U-NET architecture leverage skip connections to enhance the precision and detail of semantic segmentation outputs, and why are these connections important for backpropagation?
The U-NET architecture, introduced by Ronneberger et al. in 2015, is a convolutional neural network (CNN) designed for biomedical image segmentation. Its structure is characterized by a symmetric U-shaped architecture, which includes an encoder-decoder structure with skip connections that play a important role in enhancing the precision and detail of semantic segmentation outputs. These skip
- Published in Artificial Intelligence, EITC/AI/ADL Advanced Deep Learning, Advanced computer vision, Advanced models for computer vision, Examination review
Why is it incorrect to consider activation function running on the input data of a layer?
In the realm of deep learning, particularly when utilizing frameworks such as PyTorch, it is important to understand the role and correct application of activation functions within neural networks. One common misconception is the notion of applying the activation function directly to the input data of a layer. This approach is fundamentally flawed and undermines
- Published in Artificial Intelligence, EITC/AI/DLPP Deep Learning with Python and PyTorch, Neural network, Training model, Examination review