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 | Conference PDF | Archive PDF | Plain Text | 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. |