Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Logic Rule Guided Attribution with Dynamic Ablation
Authors: Jianqiao An, Yuandu Lai, Yahong Han77-85
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Both qualitative and quantitative experiments are conducted to evaluate the proposed DA. |
| Researcher Affiliation | Academia | Jianqiao An1,2, Yuandu Lai1,2,3, , Yahong Han1,2, 1College of Intelligence and Computing, Tianjin University, Tianjin, China 2Tianjin Key Lab of Machine Learning, Tianjin University, Tianjin, China 3Peng Cheng Laboratory, Shenzhen, China EMAIL |
| Pseudocode | Yes | Algorithm 1: Dynamic Ablation |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code for the Dynamic Ablation (DA) method, nor does it include a link to a code repository. |
| Open Datasets | Yes | Publicly available object classification datasets, namely, ILSVRC2012 (Russakovsky et al. 2015) val and CIFAR100 (Krizhevsky, Nair, and Hinton 2009) are used as input images. |
| Dataset Splits | No | The paper mentions using "ILSVRC2012 val" as input images, indicating a standard validation set was used for a dataset. However, it does not provide details on specific train/validation/test splits, percentages, or methodology for its overall experimental setup or how it handled validation within its own pipeline for all experiments. |
| Hardware Specification | Yes | We use a Ge Force GTX TITAN X GPU with 12GB of memory and the Res Net-50 as the base model in this experiment. |
| Software Dependencies | No | The paper mentions using "Py Torch model zoo" for base models and the "SLIC" algorithm, but it does not specify version numbers for any of these software components (e.g., PyTorch version, SLIC version). |
| Experiment Setup | Yes | We set the iteration number as 500, superpixel number as 500. The hyperparameters αstep, fc, and k in our method are set to 0.2, 0.95, and 4, respectively. |