AAR-CNNs: Auto Adaptive Regularized Convolutional Neural Networks
Authors: Yao Lu, Guangming Lu, Yuanrong Xu, Bob Zhang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comparative experiments are performed on low resolution Image Net, CIFAR and SVHN datasets. Experimental results show that the AAR-CNNs can achieve stateof-the-art performances on these datasets. |
| Researcher Affiliation | Academia | 1 Shenzhen Graduate School, Harbin Institute of Technology, China 2 Department of Computer and Information Science,University of Macau, Macau |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | Comparative experiments are performed on low resolution Image Net, CIFAR and SVHN datasets. [...] Low Resolution Image Net datasets [Chrabaszcz et al., 2017] are the downsampled variants of Image Net [Deng et al., 2009] [...] CIFAR [Krizhevsky and Hinton, 2009] datasets include CIFAR-10 and CIFAR-100. They both have 60, 000 32 32pixel colored nature scene images in total, including 50, 000 images for training and 10, 000 images for testing in 10 and 100 classes. [...] The Street View House Numbers (SVHN) dataset [Netzer et al., 2011] includes 73,257 training images, 26,032 testing images and 531,131 images for additional training. |
| Dataset Splits | No | For ImageNet, the paper mentions following augmentation details from a prior work but does not specify explicit train/validation/test splits. For CIFAR and SVHN, it specifies train and test set sizes, but no explicit mention of a validation split or its size/methodology is provided. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions utilizing 'SGD method' and 'Nesterov momentum' but does not specify any software libraries or frameworks with version numbers (e.g., PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | But the mini-batch size is set to 100. The training epoches are set to 40 and 80 and utilizes the SGD method and Nesterov momentum [Sutskever et al., 2013b] to optimize. The optimization starts from the initial learning rate with 0.01 and 0.1, which is respectively divided by 5 every 10 epoches and divided by 10 at 50% and 75% of the total number of training epochs. The momentum is 0.9, and the weight decay is respectively set to 5e 4 and 1e 4 on AAR-WRN and AAR-DN. |