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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Statistically Significant Concept-based Explanation of Image Classifiers via Model Knockoffs
Authors: Kaiwen Xu, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed method in our synthetic and real data experiments. |
| Researcher Affiliation | Academia | Kaiwen Xu1,3 , Kazuto Fukuchi1,3 , Youhei Akimoto1,3 and Jun Sakuma2,3 1University of Tsukuba 2Tokyo Institute of Technology 3RIKEN AIP |
| Pseudocode | Yes | Algorithm 1 Algorithm for concept selection |
| Open Source Code | No | The paper does not include any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | In our synthetic experiments, we use the Celeb A dataset [Liu et al., 2015]... Colored MNIST. We manually add six types of colors to the MNIST dataset [Le Cun et al., 1998]... |
| Dataset Splits | No | The paper states, 'We divided the dataset into two parts, the dataset DL for feature extractor training and the dataset DS for concept selection as shown in step 1.' but does not specify exact percentages, absolute sample counts, or other specific details for training, validation, or test splits. No explicit validation split is mentioned. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, memory specifications, or types of computing resources used for experiments. |
| Software Dependencies | No | The paper does not provide any specific software dependencies or library versions (e.g., Python, PyTorch, TensorFlow versions) used in the experiments. |
| Experiment Setup | Yes | The paper specifies hyperparameter values such as 'α5 = 0.25', 'α5 = 64', 'α5 = 128', and 'α2 = 5' and mentions controlling concept sparsity by adjusting α5. |