Out of spuriousity: Improving robustness to spurious correlations without group annotations

• URL: http://arxiv.org/abs/2407.14974v1
• Date: Sat, 20 Jul 2024 20:24:14 GMT
• Title: Out of spuriousity: Improving robustness to spurious correlations without group annotations
• Authors: Phuong Quynh Le, Jörg Schlötterer, Christin Seifert,
• Abstract summary: We propose an approach to extract a subnetwork from a fully trained network that does not rely on spurious correlations. The increase in the worst-group performance of our approach contributes to strengthening the hypothesis that there exists a subnetwork in a fully trained dense network.
• Score: 2.592470112714595
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning models are known to learn spurious correlations, i.e., features having strong relations with class labels but no causal relation. Relying on those correlations leads to poor performance in the data groups without these correlations and poor generalization ability. To improve the robustness of machine learning models to spurious correlations, we propose an approach to extract a subnetwork from a fully trained network that does not rely on spurious correlations. The subnetwork is found by the assumption that data points with the same spurious attribute will be close to each other in the representation space when training with ERM, then we employ supervised contrastive loss in a novel way to force models to unlearn the spurious connections. The increase in the worst-group performance of our approach contributes to strengthening the hypothesis that there exists a subnetwork in a fully trained dense network that is responsible for using only invariant features in classification tasks, therefore erasing the influence of spurious features even in the setup of multi spurious attributes and no prior knowledge of attributes labels.

Related papers

• Towards Robust Text Classification: Mitigating Spurious Correlations with Causal Learning [2.7813683000222653]
  We propose the Causally Calibrated Robust ( CCR) to reduce models' reliance on spurious correlations. CCR integrates a causal feature selection method based on counterfactual reasoning, along with an inverse propensity weighting (IPW) loss function. We show that CCR state-of-the-art performance among methods without group labels, and in some cases, it can compete with the models that utilize group labels.
  arXiv  Detail & Related papers  (2024-11-01T21:29:07Z)
• Spuriousness-Aware Meta-Learning for Learning Robust Classifiers [26.544938760265136]
  Spurious correlations are brittle associations between certain attributes of inputs and target variables. Deep image classifiers often leverage them for predictions, leading to poor generalization on the data where the correlations do not hold. Mitigating the impact of spurious correlations is crucial towards robust model generalization, but it often requires annotations of the spurious correlations in data.
  arXiv  Detail & Related papers  (2024-06-15T21:41:25Z)
• Learning Robust Classifiers with Self-Guided Spurious Correlation Mitigation [26.544938760265136]
  Deep neural classifiers rely on spurious correlations between spurious attributes of inputs and targets to make predictions. We propose a self-guided spurious correlation mitigation framework. We show that training the classifier to distinguish different prediction behaviors reduces its reliance on spurious correlations without knowing them a priori.
  arXiv  Detail & Related papers  (2024-05-06T17:12:21Z)
• Unsupervised Concept Discovery Mitigates Spurious Correlations [45.48778210340187]
  Models prone to spurious correlations in training data often produce brittle predictions and introduce unintended biases. In this paper, we establish a novel connection between unsupervised object-centric learning and mitigation of spurious correlations. We introduce CoBalT: a concept balancing technique that effectively mitigates spurious correlations without requiring human labeling of subgroups.
  arXiv  Detail & Related papers  (2024-02-20T20:48:00Z)
• Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation [63.180725016463974]
  Cross-modal retrieval relies on well-matched large-scale datasets that are laborious in practice. We introduce a novel noisy correspondence learning framework, namely textbfSelf-textbfReinforcing textbfErrors textbfMitigation (SREM)
  arXiv  Detail & Related papers  (2023-12-27T09:03:43Z)
• Stubborn Lexical Bias in Data and Models [50.79738900885665]
  We use a new statistical method to examine whether spurious patterns in data appear in models trained on the data. We apply an optimization approach to *reweight* the training data, reducing thousands of spurious correlations. Surprisingly, though this method can successfully reduce lexical biases in the training data, we still find strong evidence of corresponding bias in the trained models.
  arXiv  Detail & Related papers  (2023-06-03T20:12:27Z)
• Understanding and Mitigating Spurious Correlations in Text Classification with Neighborhood Analysis [69.07674653828565]
  Machine learning models have a tendency to leverage spurious correlations that exist in the training set but may not hold true in general circumstances. In this paper, we examine the implications of spurious correlations through a novel perspective called neighborhood analysis. We propose a family of regularization methods, NFL (doN't Forget your Language) to mitigate spurious correlations in text classification.
  arXiv  Detail & Related papers  (2023-05-23T03:55:50Z)
• Semi-Supervised Clustering with Inaccurate Pairwise Annotations [3.7384509727711923]
  This paper presents a clustering model that incorporates pairwise annotations in the form of must-link and cannot-link relations. We also extend the model to integrate prior knowledge of experts' accuracy and discuss circumstances in which the use of this knowledge is beneficial.
  arXiv  Detail & Related papers  (2021-04-05T20:37:00Z)
• Decorrelated Clustering with Data Selection Bias [55.91842043124102]
  We propose a novel Decorrelation regularized K-Means algorithm (DCKM) for clustering with data selection bias. Our DCKM algorithm achieves significant performance gains, indicating the necessity of removing unexpected feature correlations induced by selection bias.
  arXiv  Detail & Related papers  (2020-06-29T08:55:50Z)
• Learning Causal Models Online [103.87959747047158]
  Predictive models can rely on spurious correlations in the data for making predictions. One solution for achieving strong generalization is to incorporate causal structures in the models. We propose an online algorithm that continually detects and removes spurious features.
  arXiv  Detail & Related papers  (2020-06-12T20:49:20Z)

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.