Evolutionary Approach for AutoAugment Using the Thermodynamical Genetic Algorithm
Authors: Akira Terauchi, Naoki Mori9851-9858
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To confirm the effectiveness of the proposed method, computational experiments were conducted using two benchmark datasets, CIFAR-10 and SVHN, as examples. The experimental results show that the proposed method can obtain various useful augmentation sub-policies for the problems while reducing the computational cost. |
| Researcher Affiliation | Academia | Akira Terauchi, Naoki Mori Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka, Japan terauchi@ss.cs.osakafu-u.ac.jp, mori@cs.osakafu-u.ac.jp |
| Pseudocode | Yes | The pseudocode of the TDGA algorithm is provided in Algorithm 1. [...] The overall procedure is summarized in Algorithm 2. |
| Open Source Code | No | The paper does not include an unambiguous statement that the source code for the described methodology is released, nor does it provide a direct link to a code repository for it. |
| Open Datasets | Yes | To investigate the performance of TDGA Auto Augment, we applied the method to CIFAR-10 and SVHN (Netzer et al. 2011) datasets and compared the results with those of the baseline and previous methods. [...] CIFAR-10 is a benchmark dataset for image classification that has been studied for a long time, and includes 50,000 training images and 10,000 test images. |
| Dataset Splits | Yes | In TDGA Auto Augment, the training sample is divided into a training dataset Dtrain and an evaluation dataset Dvalid by stratified shuffling (Shahrokh Esfahani and Dougherty 2013). [...] In the search process, 90% of the training data were used for Dtrain, and the remaining 10% were used for Dvalid. |
| Hardware Specification | Yes | Table 3: Comparison of the number of GPU hours required by TDGA Auto Augment with those required by other methods. GPU hours GPU AA 5000 Tesla P100 PBA 5 Titan XP Fast AA 3.5 Tesla V100 TDGA AA 2.5 RTX 2080Ti |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | Pre-training was conducted for 200 epochs. [...] In the GA, the final generation was set to 30 (Ng = 30), the mutation rate of each locus was set to 0.06, and uniform crossover was used [...] For the models, we used a learning rate of 0.1, weight decay of 5e-4, batch size of 128, and cosine learning rate decay for 200 epochs. We set m = 2 for the experiments. [...] We set Np = 64, M = 5, m = 2, and T = 0.02. |