How do residual connections in ResNet architectures facilitate the training of very deep neural networks, and what impact did this have on the performance of image recognition models?

Residual connections, also known as skip connections or shortcuts, are a fundamental component of Residual Networks (ResNets), which have significantly advanced the field of deep learning, particularly in the domain of image recognition. These connections address several critical challenges associated with training very deep neural networks. The Problem of Vanishing and Exploding Gradients One of

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