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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Visual Data Diagnosis and Debiasing with Concept Graphs
Authors: Rwiddhi Chakraborty, Yinong O Wang, Jialu Gao, Runkai Zheng, Cheng Zhang, Fernando D De la Torre
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that data augmentation based on a balanced concept distribution augmented by CONBIAS improves generalization performance across multiple datasets compared to state-of-the-art methods. |
| Researcher Affiliation | Academia | 1Carnegie Mellon University 2Ui T The Arctic University of Norway 3Texas A&M University |
| Pseudocode | Yes | Algorithm 1 Rebalance Sampling |
| Open Source Code | Yes | Code: https://github.com/rwchakra/conbias |
| Open Datasets | Yes | We use three datasets in our work: Waterbirds [45], Urban Cars [22], and COCO-GB [50], that are commonly used in the bias mitigation domain. |
| Dataset Splits | Yes | In Table 5 we present the train, validation, and test splits for our three datasets. Waterbirds 4795 1199 5794 Urban Cars 8000 1000 1000 COCO-GB 32582 1331 1000 |
| Hardware Specification | Yes | We trained all models on a single NVIDIA RTX A4000 |
| Software Dependencies | No | The paper mentions 'Py Torch [35]' but does not provide a specific version number for it or other software dependencies. |
| Experiment Setup | Yes | Following previous work, we use validation loss based checkpointing to choose the best model, the Adam optimizer with a learning rate of 10 3, a weight decay of 10 5, and a cosine learning schedule over 100 epochs. |