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 output of a previous layer is added to the output of a later layer. In the case of concatenation, the outputs are concatenated along a chosen axis. The choice between addition and concatenation depends on the specific architecture and task at hand.

Skip connections have been shown to improve the performance of deep neural networks in various tasks, including image classification, object detection, and semantic segmentation. They are a key component of many state-of-the-art architectures, such as ResNet, DenseNet, and U-Net.