Bias Amplification Enhances Minority Group Performance
- URL: http://arxiv.org/abs/2309.06717v2
- Date: Tue, 9 Apr 2024 16:05:23 GMT
- Title: Bias Amplification Enhances Minority Group Performance
- Authors: Gaotang Li, Jiarui Liu, Wei Hu,
- Abstract summary: We propose BAM, a novel two-stage training algorithm.
In the first stage, the model is trained using a bias amplification scheme via introducing a learnable auxiliary variable for each training sample.
In the second stage, we upweight the samples that the bias-amplified model misclassifies, and then continue training the same model on the reweighted dataset.
- Score: 10.380812738348899
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Neural networks produced by standard training are known to suffer from poor accuracy on rare subgroups despite achieving high accuracy on average, due to the correlations between certain spurious features and labels. Previous approaches based on worst-group loss minimization (e.g. Group-DRO) are effective in improving worse-group accuracy but require expensive group annotations for all the training samples. In this paper, we focus on the more challenging and realistic setting where group annotations are only available on a small validation set or are not available at all. We propose BAM, a novel two-stage training algorithm: in the first stage, the model is trained using a bias amplification scheme via introducing a learnable auxiliary variable for each training sample; in the second stage, we upweight the samples that the bias-amplified model misclassifies, and then continue training the same model on the reweighted dataset. Empirically, BAM achieves competitive performance compared with existing methods evaluated on spurious correlation benchmarks in computer vision and natural language processing. Moreover, we find a simple stopping criterion based on minimum class accuracy difference that can remove the need for group annotations, with little or no loss in worst-group accuracy. We perform extensive analyses and ablations to verify the effectiveness and robustness of our algorithm in varying class and group imbalance ratios.
Related papers
- Trained Models Tell Us How to Make Them Robust to Spurious Correlation without Group Annotation [3.894771553698554]
Empirical Risk Minimization (ERM) models tend to rely on attributes that have high spurious correlation with the target.
This can degrade the performance on underrepresented (or'minority') groups that lack these attributes.
We propose Environment-based Validation and Loss-based Sampling (EVaLS) to enhance robustness to spurious correlation.
arXiv Detail & Related papers (2024-10-07T08:17:44Z) - The Group Robustness is in the Details: Revisiting Finetuning under Spurious Correlations [8.844894807922902]
Modern machine learning models are prone to over-reliance on spurious correlations.
In this paper, we identify surprising and nuanced behavior of finetuned models on worst-group accuracy.
Our results show more nuanced interactions of modern finetuned models with group robustness than was previously known.
arXiv Detail & Related papers (2024-07-19T00:34:03Z) - How does promoting the minority fraction affect generalization? A theoretical study of the one-hidden-layer neural network on group imbalance [64.1656365676171]
Group imbalance has been a known problem in empirical risk minimization.
This paper quantifies the impact of individual groups on the sample complexity, the convergence rate, and the average and group-level testing performance.
arXiv Detail & Related papers (2024-03-12T04:38:05Z) - Rethinking Classifier Re-Training in Long-Tailed Recognition: A Simple
Logits Retargeting Approach [102.0769560460338]
We develop a simple logits approach (LORT) without the requirement of prior knowledge of the number of samples per class.
Our method achieves state-of-the-art performance on various imbalanced datasets, including CIFAR100-LT, ImageNet-LT, and iNaturalist 2018.
arXiv Detail & Related papers (2024-03-01T03:27:08Z) - Ranking & Reweighting Improves Group Distributional Robustness [14.021069321266516]
We propose a ranking-based training method called Discounted Rank Upweighting (DRU) to learn models that exhibit strong OOD performance on the test data.
Results on several synthetic and real-world datasets highlight the superior ability of our group-ranking-based (akin to soft-minimax) approach in selecting and learning models that are robust to group distributional shifts.
arXiv Detail & Related papers (2023-05-09T20:37:16Z) - Take One Gram of Neural Features, Get Enhanced Group Robustness [23.541213868620837]
Predictive performance of machine learning models trained with empirical risk minimization can degrade considerably under distribution shifts.
We propose to partition the training dataset into groups based on Gram matrices of features extracted by an identification'' model.
Our approach not only improves group robustness over ERM but also outperforms all recent baselines.
arXiv Detail & Related papers (2022-08-26T12:34:55Z) - Correct-N-Contrast: A Contrastive Approach for Improving Robustness to
Spurious Correlations [59.24031936150582]
Spurious correlations pose a major challenge for robust machine learning.
Models trained with empirical risk minimization (ERM) may learn to rely on correlations between class labels and spurious attributes.
We propose Correct-N-Contrast (CNC), a contrastive approach to directly learn representations robust to spurious correlations.
arXiv Detail & Related papers (2022-03-03T05:03:28Z) - Towards Group Robustness in the presence of Partial Group Labels [61.33713547766866]
spurious correlations between input samples and the target labels wrongly direct the neural network predictions.
We propose an algorithm that optimize for the worst-off group assignments from a constraint set.
We show improvements in the minority group's performance while preserving overall aggregate accuracy across groups.
arXiv Detail & Related papers (2022-01-10T22:04:48Z) - Just Train Twice: Improving Group Robustness without Training Group
Information [101.84574184298006]
Standard training via empirical risk minimization can produce models that achieve high accuracy on average but low accuracy on certain groups.
Prior approaches that achieve high worst-group accuracy, like group distributionally robust optimization (group DRO) require expensive group annotations for each training point.
We propose a simple two-stage approach, JTT, that first trains a standard ERM model for several epochs, and then trains a second model that upweights the training examples that the first model misclassified.
arXiv Detail & Related papers (2021-07-19T17:52:32Z) - Examining and Combating Spurious Features under Distribution Shift [94.31956965507085]
We define and analyze robust and spurious representations using the information-theoretic concept of minimal sufficient statistics.
We prove that even when there is only bias of the input distribution, models can still pick up spurious features from their training data.
Inspired by our analysis, we demonstrate that group DRO can fail when groups do not directly account for various spurious correlations.
arXiv Detail & Related papers (2021-06-14T05:39:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.