You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: In this paper, we study a new paradigm of designing convolutional architectures and describe a scalable method to optimize convolutional architectures on a dataset of interest, for instance the ImageNet classification dataset.Our approach is inspired by the recently proposed Neural Architecture Search (NAS) framework [71], which uses a reinforcement learning search method to optimize architecture configurations. Applying NAS, or any other search methods, directly to a large dataset, such as the ImageNet dataset, is however computationally expensive. We therefore propose to search for a good architecture on a proxy dataset, for example the smaller CIFAR-10 dataset, and then transfer the learned architecture to ImageNet. We achieve this transferrability by designing a search space (which we call “the NASNet search space”) so that the complexity of the architecture is independent of the depth of the network and the size of input images. More concretely, all convolutional networks in our search space are composed of convolutional layers (or “cells”) with identical structure but different weights. Searching for the best convolutional architectures is therefore reduced to searching for the best cell structure. Searching for the best cell structure has two main benefits: it is much faster than searching for an entire network architecture and the cell itself is more likely to generalize to other problems.In our experiments, this approach significantly accelerates the search for the best architectures using CIFAR-10 by a factor of 7×7\times7 × and learns architectures that successfully transfer to ImageNet.

Question: Is the architecture search by the original NAS and NASNet are different?
From the Table 1, the depths, number of parameters vary for both original NAS and NASNet. So, we can conclude that architecture search by both is different.