A 残差接続 is a technique used in 深層学習, particularly in ニューラルネットワーク, to help improve their training and performance. The concept was popularized by the ResNet (Residual Network) architecture, which won the ImageNet 2015年のコンペティション。
In a typical neural network, data flows sequentially through layers, where each layer applies certain transformations. However, as networks become deeper (with more layers), they can experience issues such as 消失勾配, where the gradients used to update weights during training become very small, hindering learning.
残差接続は、この問題に対処するために、層への入力が1つまたは複数の層をバイパスし、それらの層の出力に直接加算されることを可能にします。これは次のように数学的に表されます：
出力 = F(入力) + 入力
Here, F(Input) represents the transformation applied by the layers being bypassed. By including the original input in the output, residual connections help maintain the flow of information and gradients, making it easier for the network to learn complex パターン。
These connections also allow for the training of much deeper networks, leading to better performance on various tasks like image recognition, 自然言語処理, and more. Overall, residual connections are a crucial innovation in modern deep learning, facilitating the development of more sophisticated AI models.