Kernel-convoluted Deep Neural Networks with Data Augmentation
Authors: Minjin Kim, Young-geun Kim, Dongha Kim, Yongdai Kim, Myunghee Cho Paik8155-8162
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using CIFAR-10 and CIFAR-100 datasets, our experiments demonstrate that the KCM with the Mixup outperforms the Mixup method in terms of generalization and robustness to adversarial examples. |
| Researcher Affiliation | Academia | Minjin Kim, 1 Young-geun Kim, 1 Dongha Kim, 1 Yongdai Kim, 2 Myunghee Cho Paik 1 1 Department of Statistics, Seoul National University 2 School of Data Science, Seoul National University |
| Pseudocode | Yes | Algorithm 1 Training Mixup with KCM |
| Open Source Code | Yes | The source-code for conducting our experiments of binary classification on the two-moon dataset and CIFAR-10 (cat vs. dog) and multi-class classification on CIFAR-10 is available at https://github.com/MJ1021/kcm-code. |
| Open Datasets | Yes | Using CIFAR-10 and CIFAR-100 datasets, our experiments demonstrate that the KCM with the Mixup outperforms the Mixup method in terms of generalization and robustness to adversarial examples. The CIFAR-10 dataset consists of 60000 RGB images in 10 classes, with 6000 images per class. The CIFAR-100 dataset is similar to CIFAR-10, except it has 100 classes containing 600 images each. Both datasets have 50000 training images and 10000 test images. |
| Dataset Splits | No | The paper mentions 50000 training images and 10000 test images for CIFAR-10/100, but does not explicitly describe a separate validation split or how it was used to reproduce the experiments. While common practice, it is not explicitly stated within the paper. |
| Hardware Specification | No | The paper mentions using 'Res Net-34' which is a model architecture, not specific hardware. No details about CPU, GPU, or other computing resources used for experiments are provided. |
| Software Dependencies | No | The paper states 'use the author s official code' and 'add code for the local averaging part'. While it implies software usage, it does not specify any software components with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | To make a direct comparison with the original Mixup using CIFAR-10/100, we adopt the experimental configuration in the Mixup paper (Zhang et al. 2018) and use the author s official code. We use Res Net-34, which is one of the architectures from the official code. For every pixel, the maximum perturbation levels are 0.031 and 0.03 for CIFAR-10 and CIFAR-100, respectively. The number of iterations for I-FGSM is 10. The performance of the methods is measured by the median of test accuracies of the last 10 epochs. |