Explainable Models with Consistent Interpretations
Authors: Vipin Pillai, Hamed Pirsiavash2431-2439
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform all our experiments on Image Net (Deng et al. 2009) and MS-COCO (Lin et al. 2014) datasets. Tables 1 and 2 show the results using the evaluation metrics from section 4.3 on the Image Net and MS-COCO datasets respectively. |
| Researcher Affiliation | Academia | Vipin Pillai, Hamed Pirsiavash University of Maryland, Baltimore County |
| Pseudocode | No | The paper describes its method verbally and mathematically but does not include a structured pseudocode block or algorithm. |
| Open Source Code | Yes | The code and models are publicly available. |
| Open Datasets | Yes | We perform all our experiments on Image Net (Deng et al. 2009) and MS-COCO (Lin et al. 2014) datasets. |
| Dataset Splits | Yes | For evaluation, we use the validation set of 50k images for Image Net and 40k images for MS-COCO dataset. |
| Hardware Specification | Yes | We use Py Torch (Paszke et al. 2019) along with Nvidia Titan RTX and 2080Ti GPUs for training and evaluating our models. |
| Software Dependencies | No | The paper mentions using Py Torch but does not provide specific version numbers for PyTorch or any other software libraries or dependencies used in the experiments. |
| Experiment Setup | Yes | For training the models on the Image Net dataset, we use SGD with a learning rate of 0.1 for Res Net18 and 0.01 for Alex Net decayed by 0.1 every 30 epochs. We set the λ hyperparameter in Eq (5) to 25 for the Image Net experiments and 1 for the MS-COCO experiments respectively. |