Just Train Twice: Improving Group Robustness without Training Group Information (2107.09044v2)

Abstract: Standard training via empirical risk minimization (ERM) can produce models that achieve high accuracy on average but low accuracy on certain groups, especially in the presence of spurious correlations between the input and label. Prior approaches that achieve high worst-group accuracy, like group distributionally robust optimization (group DRO) require expensive group annotations for each training point, whereas approaches that do not use such group annotations typically achieve unsatisfactory worst-group accuracy. In this paper, 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. Intuitively, this upweights examples from groups on which standard ERM models perform poorly, leading to improved worst-group performance. Averaged over four image classification and natural language processing tasks with spurious correlations, JTT closes 75% of the gap in worst-group accuracy between standard ERM and group DRO, while only requiring group annotations on a small validation set in order to tune hyperparameters.

• The paper introduces the two-stage 'Just Train Twice' method that enhances group robustness without needing extensive group annotations.
• It leverages an initial ERM phase to identify misclassified instances, which are then upweighted to target weak performance areas.
• The method closes 75% of the gap between standard ERM and group DRO, improving worst-group accuracy by an average of 16.2%.

A Critical Examination of "Just Train Twice: Improving Group Robustness without Training Group Information"

This paper introduces the "Just Train Twice" (Jtt) methodology, a practical algorithm for enhancing group robustness in machine learning models, circumventing the need for group annotations during training. The primary innovation is a two-stage training process that capitalizes on empirical risk minimization (ERM) frameworks to circumvent spurious correlations that often degrade performance on minority groups. This essay critically examines the methodology, results, and implications for current and future research.

Methodology

The paper tackles a noted challenge in machine learning—uneven performance across pre-defined groups due to spurious correlations in the data. Standard ERM approaches are known for achieving average accuracy at the cost of worst-case group performance. Contrastingly, methods like group distributionally robust optimization (DRO) directly address worst-group performance but are expensive due to the need for exhaustive group annotations. Jtt offers a nuanced solution by introducing a pragmatic two-stage training approach: initially adapting standard ERM to identify misclassified instances and subsequently upweighting these examples during the final model training. This method only necessitates minimal group annotations from a validation set for hyperparameter tuning, a significant reduction in annotation costs and complexity as compared to group DRO.

Empirical Results

Jtt's efficacy is empirically validated across multiple datasets, including Waterbirds, CelebA, MultiNLI, and CivilComments-WILDS, which are known to contain spurious correlations. The results are striking: Jtt manages to close 75% of the gap between standard ERM and the more annotation-intensive group DRO in terms of worst-group accuracy while maintaining competitive average accuracy. Specifically, it improves worst-group performance by an average of 16.2% across datasets.

The compositional analysis of the Jtt error sets reveals that these sets are enriched with instances from groups that standard ERM models struggle with the most. Across datasets, the precisional enrichment of worst-group examples indicates Jtt’s potential in focusing model learning on more challenging segments of data, which ERM models tend to underperform on.

Contrasts with Existing Methods

While conceptually aligned with CVaR DRO in upweighting high-loss examples, Jtt differentiates itself by using a static error set identified by the initial model, as opposed to CVaR DRO, which dynamically identifies examples with each training iteration. This static approach in Jtt is cited as essential for its superior performance, as demonstrated by controlled variations where dynamically altering the error set yielded less favorable results. This distinction underscores the pivotal role of maintaining a consistent set of difficult examples throughout training to effectively bootstrap model robustness without resorting to computationally expensive group annotations.

Implications and Future Work

The implications of this research extend beyond immediate performance gains. The methodological simplicity and cost-effectiveness of Jtt suggest it could serve as a viable option for practitioners seeking robustness amidst limited annotation budgets. Furthermore, Jtt may possess broader applicability beyond the confines of spurious correlation robustness. Its foundational principles could be adapted for various robustness challenges, such as adversarial instances or domain generalization, where similar issues of over-reliance on spurious or biased features may arise.

Nevertheless, challenges remain. The method's reliance on validation set group annotations for hyperparameter tuning presupposes awareness and recognition of group characteristics, which might not always be feasible or practical. Future research could investigate adaptive mechanisms that further autonomize the hyperparameter selection, potentially through meta-learning or self-supervised techniques that obviate the need for group-labeled validation data.

In conclusion, the Jtt approach represents a noteworthy advancement in the ongoing effort to address group fairness and model robustness gaps in machine learning. Its contribution lies not just in enhancing model performance but also in paving the way for more accessible and less resource-intensive solutions in tackling the perennial issue of spurious correlations in AI systems.