DeepVisualInsight: Time-Travelling Visualization for Spatio-Temporal Causality of Deep Classification Training
Authors: Xianglin Yang, Yun Lin, Ruofan Liu, Zhenfeng He, Chao Wang, Jin Song Dong, Hong Mei5359-5366
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments show that, comparing to baseline approaches, we achieve the best visualization performance regarding the spatial/temporal properties and visualization efficiency. Moreover, our case study shows that our visual solution can well reflect the characteristics of various training scenarios, showing good potential of DVI as a debugging tool for analyzing deep learning training processes. |
| Researcher Affiliation | Collaboration | 1School of Computing, National University of Singapore, Singapore 2Key Lab of High-Confidence Software Technology, Mo E (Peking University), China |
| Pseudocode | No | The paper describes algorithms and methods but does not present them in a structured pseudocode block or a clearly labeled algorithm section. |
| Open Source Code | Yes | More details of our tool/experiments are at (DVI 2021). DVI. 2021. DVI (Anonymous). https://sites.google.com/ view/deepvisualinsight/. Accessed: 2021-03-17. |
| Open Datasets | Yes | We choose three datasets, i.e., MNIST, Fashion-MNIST, and CIFAR-10. We use Res Net18 (He et al. 2016) as the subject classifier |
| Dataset Splits | No | The paper mentions training, testing, but does not explicitly state how validation data splits are used or if a separate validation set is employed for hyperparameter tuning. It refers to "subset training/testing sizes are set to 1000/200" for Deep View comparisons, but no general explicit split. |
| Hardware Specification | No | The paper does not specify the hardware used for running experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper mentions using ResNet18 and common deep learning concepts but does not specify any software libraries or frameworks with their version numbers (e.g., PyTorch, TensorFlow, scikit-learn versions). |
| Experiment Setup | Yes | Given the dimension of the feature vector is h, we let the encoder and decoder to have shape (h, h / 2 , 2); and (2, h / 2 , h) respectively. Learning rate is initialized with 0.01 and decay every 8 epochs by factor of 10. The threshold δ to decide boundary point is set to be 0.1. We generate 0.1 N boundary points, shared by all the solutions. The upper bound for λ in boundary point generation is set to 0.4, α in Equation 5 to 0.8, β in Equation 8 to 1.0, and the trade-off hyper-parameters in total loss (Equation 12) to 1.0, 1.0, 0.3 respectively. |