skipyhteydet
Skipyhteydet, or "skip connections" in English, are a technique used in deep learning, particularly in convolutional neural networks (CNNs). They involve adding connections between non-adjacent layers in a neural network, allowing the network to learn both high-level and low-level features simultaneously. This technique helps mitigate the vanishing gradient problem, which can occur when training very deep networks, by providing a direct path for gradients to flow through the network during backpropagation.
Skip connections are typically implemented using element-wise addition or concatenation. In the case of addition, the
Skip connections have been shown to improve the performance of deep neural networks in various tasks, including