Explaining Generalization Power of a DNN Using Interactive Concepts
Authors: Huilin Zhou, Hao Zhang, Huiqi Deng, Dongrui Liu, Wen Shen, Shih-Han Chan, Quanshi Zhang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Specifically, we trained a seven-layer MLP (MLP-7-census) on the census dataset (Asuncion and Newman 2007) and a seven-layer MLP (MLP-7-TV) on the TV news dataset (Asuncion and Newman 2007), respectively. We also trained Alex Net (Krizhevsky, Sutskever, and Hinton 2017), Res Net-20 (He et al. 2016), and VGG-11 (Simonyan and Zisserman 2014) on the MNIST dataset (Le Cun et al. 1998) (Alex Net-MNIST, Res Net-20-MNIST, VGG-11-MNIST) and the CIFAR-10 dataset (Krizhevsky, Hinton et al. 2009) (Alex Net-CIFAR10, Res Net-20-CIFAR-10, VGG-11-CIFAR-10). ... Various experiments have verified our findings. |
| Researcher Affiliation | Academia | Huilin Zhou1, Hao Zhang1, Huiqi Deng1, Dongrui Liu1, Wen Shen1, Shih-Han Chan1, 2, Quanshi Zhang1* 1Shanghai Jiao Tong University 2University of California San Diego {zhouhuilin116, 1603023-zh, denghq7, drliu96, wen shen}@sjtu.edu.cn s2chan@ucsd.edu, zqs1022@sjtu.edu.cn |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The code will be released when the paper is accepted. |
| Open Datasets | Yes | Specifically, we trained a seven-layer MLP (MLP-7-census) on the census dataset (Asuncion and Newman 2007) and a seven-layer MLP (MLP-7-TV) on the TV news dataset (Asuncion and Newman 2007), respectively. We also trained Alex Net (Krizhevsky, Sutskever, and Hinton 2017), Res Net-20 (He et al. 2016), and VGG-11 (Simonyan and Zisserman 2014) on the MNIST dataset (Le Cun et al. 1998) (Alex Net-MNIST, Res Net-20-MNIST, VGG-11-MNIST) and the CIFAR-10 dataset (Krizhevsky, Hinton et al. 2009) (Alex Net-CIFAR10, Res Net-20-CIFAR-10, VGG-11-CIFAR-10). |
| Dataset Splits | No | The paper mentions 'training samples' and 'testing samples' but does not provide explicit details about train/validation/test dataset splits (e.g., percentages, sample counts, or specific split files) needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | Specifically, we trained a seven-layer MLP (MLP-7-census) on the census dataset... Each layer of the MLPs contained 100 neurons. ... We trained Alex Net, Res Net20 and VGG-11 with ρ = 0, 0.05, 0.1, 0.2, 0.3 noise on the MNIST dataset and the CIFAR-10 dataset. ... We trained a five-layer MLP on the dataset X = {0, 1}n... Here, n = 10. |