ResNet, or Residual Network, is a type of deep learning architecture designed to improve the training of convolutional neural networks by introducing skip connections, or shortcuts, that bypass one or more layers. This innovative design helps alleviate the vanishing gradient problem, allowing for the training of very deep networks without losing performance. ResNet is particularly significant in the context of image analysis as it enhances feature learning and enables better accuracy in tasks such as image classification and object detection.
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