Description: Bottleneck architecture in neural networks refers to a design that includes specific layers that limit the amount of information flowing through the network at certain points. This approach is used to reduce the complexity of the model and improve efficiency in data processing. In the context of neural networks, bottleneck layers allow the network to focus on the most relevant features of data, facilitating the learning of patterns over time. In generative adversarial networks (GANs), these layers help generate more compact and effective representations, which is crucial for the quality of generated images or data. In convolutional neural networks (CNNs), bottleneck layers are used to reduce the dimensionality of extracted features, optimizing the network’s performance and speed. Overall, this architecture is essential for balancing the learning capacity of the network with the need to avoid overfitting and improve the model’s generalization in complex tasks.
