LISSNAS: Locality-based Iterative Search Space Shrinkage for Neural Architecture Search
Authors: Bhavna Gopal, Arjun Sridhar, Tunhou Zhang, Yiran Chen
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We showcase our method on an array of search spaces spanning various sizes and datasets. We accentuate the effectiveness of our shrunk spaces when used in one-shot search by achieving the best Top-1 accuracy in two different search spaces. Our method achieves a SOTA Top-1 accuracy of 77.6% in Image Net under mobile constraints, bestin-class Kendal-Tau, architectural diversity, and search space size. |
| Researcher Affiliation | Academia | Bhavna Gopal1 , Arjun Sridhar1 , Tunhou Zhang1 and Yiran Chen1 1Duke University {bhavna.gopal, arjun.sridhar, tunhou.zhang, yiran.chen}@duke.edu |
| Pseudocode | No | The paper describes the algorithm steps and shows a pipeline diagram (Figure 3) but does not include structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not explicitly state that source code for the described methodology is available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We investigate locality and our algorithm s performance on NASBench101, Trans NASBench Macro, NASBench301, and Shuffle Net V2. For a more detailed description of these search spaces and respective datasets, please reference the Appendix C. ... Image Net under mobile constraints serves as a canonical benchmark for all NAS techniques. |
| Dataset Splits | No | The paper mentions using training and evaluation but does not provide specific details on the split percentages or methodology for validation datasets. |
| Hardware Specification | No | The paper mentions '4 GPU days' but does not specify the type or model of GPU, CPU, or any other specific hardware used for the experiments. |
| Software Dependencies | No | The paper mentions using 'XGBBoost as our accuracy predictor' but does not provide specific version numbers for XGBoost or any other software dependencies. |
| Experiment Setup | No | The paper mentions a 'hyperparameter optimization scheme' but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or other detailed training configurations in the main text. |