Spread Spurious Attribute: Improving Worst-group Accuracy with Spurious Attribute Estimation
Authors: Junhyun Nam, Jaehyung Kim, Jaeho Lee, Jinwoo Shin
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on various benchmark datasets show that our algorithm consistently outperforms the baseline methods using the same number of group-labeled samples. |
| Researcher Affiliation | Academia | Junhyun Nam1, Jaehyung Kim1, Jaeho Lee2 , Jinwoo Shin1 1KAIST, 2POSTECH {junhyun.nam,jaehyungkim,jinwoos}@kaist.ac.kr jaeho.lee@postech.ac.kr |
| Pseudocode | Yes | Algorithm 1 Spread Spurious Attribute |
| Open Source Code | Yes | Also, we provide our source code as a part of the open-to-public supplementary materials. |
| Open Datasets | Yes | Waterbirds (Sagawa et al., 2020)... Caltech-UCSD Birds dataset (Wah et al., 2011) with landscapes from Places (Zhou et al., 2017)., Celeb A (Liu ets al., 2015), Multi NLI (Williams et al., 2018), Civil Comments-WILDS (Borkan et al., 2019; Koh et al., 2021), CIFAR-10 (Krizhevsky et al., 2009). |
| Dataset Splits | Yes | For all datasets, we use the validation split of the dataset as the group-labeled set. and We use D L, D U to train the spurious attribute predictor that make prediction on D U, and validate the model with D L. |
| Hardware Specification | Yes | In Table 10, we provide the time required for the pseudo-labeling phase and the robust training phase on a single Nvidia Titan XP for each dataset. |
| Software Dependencies | No | The paper mentions software like torchvision and huggingface implementations, and optimizers like SGD and Adam W, but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | For Waterbirds and Celeb A, we tuned the learning rate over {1e3, 1e-4, 1e-5} and ℓ2 regularization over {1e-1, 1e-4}. We used SGD optimizer with momentum 0.9 and batch size 64. In pseudo-labeling phase, we train the spurious attribute predictor 1k iterations for Waterbirds and 45k iterations for Celeb A. |