How are adversarial neighbors connected to the original samples to construct the structure in neural structure learning?
Adversarial learning is a technique used in neural structure learning to improve the robustness and generalization of neural network models. In this approach, adversarial neighbors are connected to the original samples to construct the structure in neural structure learning. These adversarial neighbors are generated by perturbing the original samples in a way that maximizes the
How does neural structure learning optimize both sample features and structured signals to improve neural networks?
Neural structure learning plays a important role in optimizing both sample features and structured signals to enhance the performance of neural networks. By incorporating structured signals into the learning process, neural networks can leverage additional information beyond individual sample features, leading to improved generalization and robustness. In the context of artificial intelligence, specifically in the