Posterior-Guided Neural Architecture Search
Authors: Yizhou Zhou, Xiaoyan Sun, Chong Luo, Zheng-Jun Zha, Wenjun Zeng6973-6980
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our PGNAS method on the fundamental image classification task. Results on Cifar-10, Cifar-100 and Image Net show that PGNAS achieves a good trade-off between precision and speed of search among NAS methods. For example, it takes 11 GPU days to search a very competitive architecture with 1.98% and 14.28% test errors on Cifar10 and Cifar100, respectively. |
| Researcher Affiliation | Collaboration | 1University of Science Technology of China, 2Microsoft Research Asia |
| Pseudocode | Yes | Algorithm 1: PGNAS |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | Yes | We validate our PGNAS method on the fundamental image classification task. Results on Cifar-10, Cifar-100 and Image Net show that PGNAS achieves a good trade-off between precision and speed of search among NAS methods. |
| Dataset Splits | Yes | Then the sampled candidates are evaluated and ranked on a held-out validation dataset. |
| Hardware Specification | Yes | Our PGNAS is trained in an end-to-end way with the Stochastic Gradient Descent (SGD) using a single P40 GPU card for Cifar-10/Cifar-100 and 4 M40 GPU cards for Image Net. |
| Software Dependencies | No | The paper mentions using Stochastic Gradient Descent (SGD) as an optimization method but does not provide specific version numbers for any software libraries, frameworks (e.g., TensorFlow, PyTorch), or programming languages used. |
| Experiment Setup | Yes | Our PGNAS is trained in an end-to-end way with the Stochastic Gradient Descent (SGD) ... For every super-network, we insert a dropout layer after each convolution layer according to Eq. 16 to facilitate the computation of Eq. 15. ... Please refer to the supplementary material for more details of the supernetworks and all hyper-parameter settings used in this paper. ... Table. 5 shows the impact of temperature value τ. ... Table. 6. The performance improves along with the increase of number of samples. |