Neural Graph Embedding for Neural Architecture Search
Authors: Wei Li, Shaogang Gong, Xiatian Zhu4707-4714
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show the superiority of NGE over the state-of-the-art methods on image classification and semantic segmentation. |
| Researcher Affiliation | Academia | 1Queen Mary University of London, 2University of Surrey |
| Pseudocode | Yes | Algorithm 1: Neural Graph Embedding (NGE) for NAS |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | CIFAR. Both CIFAR-10 and CIFAR-100 (Krizhevsky and others 2009)... Image Net. For the large-scale image classification evaluation, we used the ILSVRC2012, a subset of Image Net (Russakovsky et al. 2015)... PASCAL VOC 2012. We used the PASCAL VOC 2012 (Everingham et al. 2015) for semantic segmentation evaluation. |
| Dataset Splits | Yes | We split 25K images from the training set for validation. |
| Hardware Specification | Yes | With NGE, the search on CIFAR-10 took only 2.4 hours on a single NVIDIA Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions optimizers (SGD, Adam) and activation functions (ReLU) but does not provide specific version numbers for any software libraries, frameworks, or programming languages used. |
| Experiment Setup | Yes | For the network parameter w, we used SGD with an initial learning rate 0.025 and the momentum of 0.9. We decayed the learning rate to 0 during training using a cosine schedule. A weight decay of 3 × 10−4 was imposed to avoid over-fitting. For the NGE learning, we used the Adam optimiser with a fixed learning rate 6 × 10−4 and set the weight decay to 1 × 10−3. To search the normal cell and reduction cell efficiently, we used 25 epochs for training the proxy network. |