Generative Adversarial Neural Architecture Search
Authors: Seyed Saeed Changiz Rezaei, Fred X. Han, Di Niu, Mohammad Salameh, Keith Mills, Shuo Lian, Wei Lu, Shangling Jui
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that GA-NAS beats the best published results under several cases on three public NAS benchmarks. In the meantime, GA-NAS can handle ad-hoc search constraints and search spaces. We show that GA-NAS can be used to improve already optimized baselines found by other NAS methods, including Efficient Net and Proxyless NAS, in terms of Image Net accuracy or the number of parameters, in their original search space. |
| Researcher Affiliation | Collaboration | 1Huawei Technologies Canada Co., Ltd. 2Department of Electrical and Computer Engineering, University of Alberta 3Huawei Kirin Solution, Shanghai, China |
| Pseudocode | Yes | Algorithm 1 GA-NAS Algorithm; Algorithm 2 Training Discriminator D and the Generator G |
| Open Source Code | No | The paper does not provide an explicit statement or a link indicating the availability of its source code. |
| Open Datasets | Yes | To evaluate search algorithm and decouple it from the impact of search spaces, we query three NAS benchmarks: NAS-Bench-101 [Ying et al., 2019], NAS-Bench-201 [Dong and Yang, 2020], and NAS-Bench-301 [Siems et al., 2020]. ... We test GA-NAS on NAS-Bench-201 by conducting 20 runs for CIFAR-10, CIFAR-100, and Image Net-16-120 using the true test accuracy. |
| Dataset Splits | Yes | We use the supernet to evaluate the accuracy of a cell on a validation set of 10k instances of CIFAR10 (see Appendix). |
| Hardware Specification | Yes | the search cost for a run is 8 GPU hours on Ge Force GTX 1080 Ti. ... Total search time including supernet training is around 680 GPU hours on Tesla V100 GPUs. ... GA-NAS for 38 hours on 8 Tesla V100 GPUs, |
| Software Dependencies | No | The paper mentions various algorithms and model architectures like RNN, GNN, MLP, and training methods such as SGD and PPO, but does not provide specific software library names with version numbers (e.g., TensorFlow, PyTorch, CUDA versions). |
| Experiment Setup | Yes | In the first setup, we set |X0| = 50, |Xt| = |Xt 1| + 50, t ≥ 1, and k = 25, In the second setup, we set |X0| = 100, |Xt| = |Xt 1| + 100, t ≥ 1, and k = 50. For both setups, the initial set X0 is picked to be a random set, and the number of iterations T is 10. |