Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets
Authors: Hayeon Lee, Eunyoung Hyung, Sung Ju Hwang
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to validate Meta D2A framework. First, we compare our model with conventional NAS methods on NAS-Bench-201 search space in Section 4.1. Second, we compare our model with transferable NAS method under a large search space in Section 4.2. Third, we compare our model with other Meta-NAS approaches on few-shot classification tasks in Section 4.3. Finally, we analyze the effectiveness of our framework in Section 4.4. |
| Researcher Affiliation | Collaboration | Hayeon Lee1 , Eunyoung Hyung1 , Sung Ju Hwang1,2 KAIST1, AITRICS2, South Korea {hayeon926, eunyoung0301, sjhwang82}@kaist.ac.kr |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. The methods are described in textual form and equations. |
| Open Source Code | Yes | Code is available at https://github.com/Hayeon Lee/Meta D2A. |
| Open Datasets | Yes | We meta-learn the proposed Meta D2A on subsets of Image Net-1K and neural architectures from the NAS-Bench201 search space. We compile Image Net-1K (Deng et al., 2009) as multiple sub-sets by randomly sampling 20 classes with an average of 26K images for each sub-sets and assign them to each task. We apply our model trained from source database to 6 benchmark datasets such as 1) CIFAR-10 (Krizhevsky et al., 2009), 2) CIFAR-100 (Krizhevsky et al., 2009), 3) MNIST (Le Cun & Cortes, 2010), 4) SVHN (Netzer et al., 2011), 5) Aircraft (Maji et al., 2013), and 6) Oxford IIIT Pets (Parkhi et al., 2012). |
| Dataset Splits | Yes | We collect Nτ =1,310/4,230 meta-training tasks for the generator/predictor and 400/400 meta-validation tasks for them, respectively. For CIFAR10 and CIFAR100, we used the training, validation, and test splits from the NAS-Bench-201, and use random validation/test splits for MNIST, SVHN, Aircraft, and Oxford-IIIT Pets by splitting the test set into two subsets of the same size. The validation set is used to update the searching algorithms as a supervision signal and the test set is used to evaluate the performance of the searched architectures. |
| Hardware Specification | Yes | Our model is performed with a single Nvidia 2080ti GPU. Search times of Meta D2A for CIFAR-10, CIFAR-100, Aircraft, and Oxford-IIIT Pets are within 57, 195, 77, and 170 GPU seconds on average with a single Nvidia RTX 2080ti GPU respectively |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python version, specific deep learning framework versions like PyTorch or TensorFlow, or CUDA versions). |
| Experiment Setup | Yes | Table 6: Hyperparameter setting of Meta D2A on NAS-Bench-201 Search Space lists values for learning rate, batch size, training epoch, and other model-specific dimensions. Section E.3 also details training strategies such as using embedding features from a pretrained ResNet18 and teacher forcing. |