Learning Robust Classifiers with Self-Guided Spurious Correlation Mitigation

• URL: http://arxiv.org/abs/2405.03649v1
• Date: Mon, 6 May 2024 17:12:21 GMT
• Title: Learning Robust Classifiers with Self-Guided Spurious Correlation Mitigation
• Authors: Guangtao Zheng, Wenqian Ye, Aidong Zhang,
• Abstract summary: 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.
• Score: 26.544938760265136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural classifiers tend to rely on spurious correlations between spurious attributes of inputs and targets to make predictions, which could jeopardize their generalization capability. Training classifiers robust to spurious correlations typically relies on annotations of spurious correlations in data, which are often expensive to get. In this paper, we tackle an annotation-free setting and propose a self-guided spurious correlation mitigation framework. Our framework automatically constructs fine-grained training labels tailored for a classifier obtained with empirical risk minimization to improve its robustness against spurious correlations. The fine-grained training labels are formulated with different prediction behaviors of the classifier identified in a novel spuriousness embedding space. We construct the space with automatically detected conceptual attributes and a novel spuriousness metric which measures how likely a class-attribute correlation is exploited for predictions. We demonstrate that training the classifier to distinguish different prediction behaviors reduces its reliance on spurious correlations without knowing them a priori and outperforms prior methods on five real-world datasets.

Related papers

• Out of spuriousity: Improving robustness to spurious correlations without group annotations [2.592470112714595]
  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.
  arXiv  Detail & Related papers  (2024-07-20T20:24:14Z)
• 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)
• 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)
• Identifying Spurious Correlations using Counterfactual Alignment [5.782952470371709]
  Models driven by spurious correlations often yield poor generalization performance. We propose the counterfactual (CF) alignment method to detect and quantify spurious correlations.
  arXiv  Detail & Related papers  (2023-12-01T20:16:02Z)
• Controlling Learned Effects to Reduce Spurious Correlations in Text Classifiers [6.662800021628275]
  We propose an algorithm to regularize the learnt effect of the features on the model's prediction to the estimated effect of feature on label. On toxicity and IMDB review datasets, the proposed algorithm minimises spurious correlations and improves the minority group.
  arXiv  Detail & Related papers  (2023-05-26T12:15:54Z)
• 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)
• Self-supervised debiasing using low rank regularization [59.84695042540525]
  Spurious correlations can cause strong biases in deep neural networks, impairing generalization ability. We propose a self-supervised debiasing framework potentially compatible with unlabeled samples. Remarkably, the proposed debiasing framework significantly improves the generalization performance of self-supervised learning baselines.
  arXiv  Detail & Related papers  (2022-10-11T08:26:19Z)
• Self-Certifying Classification by Linearized Deep Assignment [65.0100925582087]
  We propose a novel class of deep predictors for classifying metric data on graphs within PAC-Bayes risk certification paradigm. Building on the recent PAC-Bayes literature and data-dependent priors, this approach enables learning posterior distributions on the hypothesis space.
  arXiv  Detail & Related papers  (2022-01-26T19:59:14Z)
• Identifying Spurious Correlations for Robust Text Classification [9.457737910527829]
  We propose a method to distinguish spurious and genuine correlations in text classification. We use features derived from treatment effect estimators to distinguish spurious correlations from "genuine" ones. Experiments on four datasets suggest that using this approach to inform feature selection also leads to more robust classification.
  arXiv  Detail & Related papers  (2020-10-06T03:49:22Z)
• Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations [65.05561023880351]
  Adversarial examples are malicious inputs crafted to induce misclassification. This paper studies a complementary failure mode, invariance-based adversarial examples. We show that defenses against sensitivity-based attacks actively harm a model's accuracy on invariance-based attacks.
  arXiv  Detail & Related papers  (2020-02-11T18:50:23Z)
• Certified Robustness to Label-Flipping Attacks via Randomized Smoothing [105.91827623768724]
  Machine learning algorithms are susceptible to data poisoning attacks. We present a unifying view of randomized smoothing over arbitrary functions. We propose a new strategy for building classifiers that are pointwise-certifiably robust to general data poisoning attacks.
  arXiv  Detail & Related papers  (2020-02-07T21:28:30Z)

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.