Highway networks are a type of deep neural network architecture that allows information to flow freely across many layers, overcoming the challenges of training very deep networks. Inspired by Long Short-Term Memory (LSTM) networks, highway networks use adaptive gating units to regulate information flow. These gates, called transform and carry gates, enable the network to selectively pass or transform information, making it possible to train very deep networks using simple gradient descent. This architecture allows for efficient training and generalization, even with hundreds of layers. The paper discusses the importance of network depth in achieving high performance in supervised machine learning. While deeper networks can represent certain function classes more efficiently, training them remains challenging. Previous approaches to address this include better optimizers, initialization strategies, and skip connections. However, these methods often have limitations, especially as depth increases. Highway networks introduce a novel solution by allowing information to flow through "information highways," which are paths where information can travel across many layers without attenuation. This is achieved through an LSTM-inspired adaptive gating mechanism. The transform gate controls how much of the output is produced by transforming the input, while the carry gate determines how much of the input is carried forward. By setting the carry gate to 1 - transform gate, the network can effectively choose between passing the input or transforming it. Experiments show that highway networks can be trained directly using stochastic gradient descent, even with hundreds of layers, and they perform well on various tasks, including image classification. The paper also demonstrates that highway networks can be used to train deep networks more efficiently than traditional methods, with results that match or exceed those of other approaches. The architecture's flexibility and ease of training make it a promising solution for deep learning applications.Highway networks are a type of deep neural network architecture that allows information to flow freely across many layers, overcoming the challenges of training very deep networks. Inspired by Long Short-Term Memory (LSTM) networks, highway networks use adaptive gating units to regulate information flow. These gates, called transform and carry gates, enable the network to selectively pass or transform information, making it possible to train very deep networks using simple gradient descent. This architecture allows for efficient training and generalization, even with hundreds of layers. The paper discusses the importance of network depth in achieving high performance in supervised machine learning. While deeper networks can represent certain function classes more efficiently, training them remains challenging. Previous approaches to address this include better optimizers, initialization strategies, and skip connections. However, these methods often have limitations, especially as depth increases. Highway networks introduce a novel solution by allowing information to flow through "information highways," which are paths where information can travel across many layers without attenuation. This is achieved through an LSTM-inspired adaptive gating mechanism. The transform gate controls how much of the output is produced by transforming the input, while the carry gate determines how much of the input is carried forward. By setting the carry gate to 1 - transform gate, the network can effectively choose between passing the input or transforming it. Experiments show that highway networks can be trained directly using stochastic gradient descent, even with hundreds of layers, and they perform well on various tasks, including image classification. The paper also demonstrates that highway networks can be used to train deep networks more efficiently than traditional methods, with results that match or exceed those of other approaches. The architecture's flexibility and ease of training make it a promising solution for deep learning applications.