This paper introduces a novel approach to learning scalable and transferable neural network architectures for image classification. The authors propose a method to directly learn model architectures on a small dataset (CIFAR-10) and then transfer these architectures to larger datasets like ImageNet. They design a new search space, called the "NASNet search space," which enables the transferability of learned architectures. The key contribution is a new convolutional cell structure that can be stacked to form more complex architectures. The best architecture found on CIFAR-10, named NASNet, achieves state-of-the-art accuracy on ImageNet, outperforming human-designed models by 1.2% in top-1 accuracy while using 9 billion fewer FLOPS. The paper also introduces ScheduledDropPath, a regularization technique that significantly improves generalization in NASNet models. Additionally, the learned features from NASNet are found to be useful for object detection tasks, achieving state-of-the-art results on the COCO dataset. The paper demonstrates the efficiency and effectiveness of the proposed method through extensive experiments and comparisons with other state-of-the-art models.This paper introduces a novel approach to learning scalable and transferable neural network architectures for image classification. The authors propose a method to directly learn model architectures on a small dataset (CIFAR-10) and then transfer these architectures to larger datasets like ImageNet. They design a new search space, called the "NASNet search space," which enables the transferability of learned architectures. The key contribution is a new convolutional cell structure that can be stacked to form more complex architectures. The best architecture found on CIFAR-10, named NASNet, achieves state-of-the-art accuracy on ImageNet, outperforming human-designed models by 1.2% in top-1 accuracy while using 9 billion fewer FLOPS. The paper also introduces ScheduledDropPath, a regularization technique that significantly improves generalization in NASNet models. Additionally, the learned features from NASNet are found to be useful for object detection tasks, achieving state-of-the-art results on the COCO dataset. The paper demonstrates the efficiency and effectiveness of the proposed method through extensive experiments and comparisons with other state-of-the-art models.