Robust Neural Architecture Search

Robust Neural Architecture Search (RNAS) focuses on developing neural network architectures that are resilient to adversarial attacks and data perturbations, a crucial aspect for deploying models in real-world scenarios. Current research explores various approaches, including the use of novel metaheuristic algorithms (like efficient multiplayer battle game optimizers), large language models as search optimizers, and unsupervised learning methods such as masked autoencoders to reduce reliance on labeled data. These advancements aim to improve the robustness and generalization capabilities of neural networks while addressing the computational cost of traditional NAS methods, ultimately leading to more reliable and secure AI systems.