SEBRA : Debiasing through Self-Guided Bias Ranking
Authors: Adarsh Kappiyath, Abhra Chaudhuri, AJAY JAISWAL, Ziquan Liu, Yunpeng Li, Xiatian Zhu, Lu Yin
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that Sebra consistently outperforms previous state-of-the-art unsupervised debiasing techniques across multiple standard benchmarks, including Urban Cars, BAR, Celeb A, Multi NLI, and Image Net-1K. Code, pre-trained models, and training logs are available at https://kadarsh22.github.io/sebra_iclr25/. [...] 4 EXPERIMENTAL SETUP |
| Researcher Affiliation | Collaboration | 1University of Surrey, 2Fujitsu Research of Europe, 3University of Texas at Austin 4Queen Mary University of London |
| Pseudocode | Yes | We provide the pseudocode for Sebra in Algorithm 1. |
| Open Source Code | Yes | Code, pre-trained models, and training logs are available at https://kadarsh22.github.io/sebra_iclr25/. |
| Open Datasets | Yes | We evaluate the proposed ranking and debiasing strategy on one synthetic and four natural datasets with spurious correlations. The synthetic dataset, Urban Cars Li et al. (2023), focuses on car-type classification with spurious correlations involving the background and co-occurring objects. [...] For natural datasets, we use Celeb A Liu et al. (2015), [...] Additionally, we test on two natural vision datasets, BAR (Nam et al., 2020) and Image Net-1K Deng et al. (2009), and the natural language dataset Multi NLI Williams et al. (2018) [...] All the datasets used are publicly available or can be generated with publicly available resources. |
| Dataset Splits | Yes | For BAR, no bias annotations are used, even during validation and validation set is obtained by random split of training set in 80:20 ratio. |
| Hardware Specification | Yes | All the measurements were done using a single Nvidia RTX 3090 GPU. [...] All experiments were conducted using a single RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions using specific ResNet architectures (Res Net-50, Res Net-18) but does not provide specific software dependencies like Python, PyTorch/TensorFlow versions, or CUDA versions. |
| Experiment Setup | Yes | Table 7: Optimal hyper-parameters for the BAR, Urban Cars, Celeb A, and Image Net datasets determined through hyper-parameter search. Parameter Urban Cars BAR Celeb A Image Net Learning Rate (LR) 1.0 10 3 1.0 10 4 1.0 10 3 1.0 10 3 batch Size 128 64 64 512 optimiser SGD Adam SGD SGD momentum 0.1 0.8 0.5 weight decay 0.001 0 1.0 10 4 1.0 10 4 pcritical 0.75 0.75 0.7 0.1 β 1.25 1.42 1.25 1.42 γ 0.5 0.5 1 1 τ 0.05 0.15 0.05 0.1 |