Accelerating Neural Architecture Search via Proxy Data
Authors: Byunggook Na, Jisoo Mok, Hyeokjun Choe, Sungroh Yoon
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To empirically demonstrate the effectiveness, we conduct thorough experiments across diverse datasets, search spaces, and NAS algorithms. |
| Researcher Affiliation | Academia | 1 Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea 2 AIIS, ASRI, INMC, and Interdisciplinary Program in AI, Seoul National University, Seoul, South Korea |
| Pseudocode | No | The paper describes its proposed selection methods using textual descriptions and mathematical formulas, but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/nabk89/NAS-with-Proxy-data. |
| Open Datasets | Yes | NAS-Bench-1shot1 is used as the primary testing platform to observe the effect of the proxy data on the search performance of three NAS algorithms on CIFAR-10... We conduct thorough experiments across diverse datasets, search spaces, and NAS algorithms. (Datasets mentioned: CIFAR-10, ImageNet, CIFAR-100, SVHN, all standard benchmarks and implicitly publicly available and cited). |
| Dataset Splits | No | To construct proxy data of size k, k examples among 50K training examples of CIFAR-10 are selected using selection methods. The selected examples are segregated into two parts: one for updating weight parameters and the other for updating architecture parameters. However, the paper does not specify the exact proportion or number of samples in these two parts. |
| Hardware Specification | Yes | For example, executing DARTS [Liu et al., 2019] using our selection method requires only 40 GPU minutes on a single Ge Force RTX 2080ti GPU. Owing to the reduction in search cost, searching on Image Net can be completed in 7.5 GPU hours on a single Tesla V100 GPU when incorporating the proposed selection into DARTS. |
| Software Dependencies | No | The paper mentions using 'Pytorch model zoo' but does not specify version numbers for PyTorch or any other software dependencies required to replicate the experiments. |
| Experiment Setup | Yes | For a fair comparison, the same hyperparameter settings as those offered in NAS-Bench-1shot1 are used for all the tested NAS algorithms. ... It required 3.8 GPU days, i.e., 11.5 hours with eight V100 GPUs, with a batch size of 1024... PC-DARTS with ... a single V100 GPU with a batch size of 256... the weight decay factors for DARTS and Robust DARTS (L2) during search are set to be 0.0003 and 0.0243, respectively. |