This paper presents the DNA Family, a new framework for boosting the effectiveness of weight-sharing Neural Architecture Search (NAS) by dividing large search spaces into smaller blocks and applying block-wise supervisions. The approach demonstrates …
ZeroNAS presents a differentiable generative adversarial network architecture search method specifically designed for zero-shot learning (ZSL). The approach optimizes both generator and discriminator architectures, leading to significant improvements …
This work addresses the catastrophic forgetting problem in one-shot neural architecture search by treating supernet training as a constrained optimization problem. The proposed method uses a novelty search-based architecture selection approach to …
Deep learning has made substantial breakthroughs in many fields due to its powerful automatic representation capabilities. It has been proven that neural architecture design is crucial to the feature representation of data and the final performance. …