Synapse
⌘+K
Synapse
PulseExploreJournal ClubResearchersJournals
Instagram
HomeJournal ClubExplore
July 28, 2026Machine LearningOpen Access

Invariance Pair Guidance: Robustness to Spurious Correlations via Corrective Gradients

View Full Paper
Ask AI
Bookmark
Share

Authors

MSMartin SurnerAKAbdelmajid KhelilLBLudwig Bothmann

Discussion

Loading...

Member takes

Overview

Randomized trial demonstrates improved robustness to spurious correlations using Invariance Pair Guidance in diverse datasets, suggesting better model performance.

Key Points

  • This research aims to develop a method to enhance machine learning models' robustness against spurious correlations using minimal supervision.
  • Proposed Invariance Pair Guidance (IPG) method with a dual-update mechanism.
  • Generated input pairs isolating spurious attributes to define invariance.
  • Conducted experiments on ColoredMNIST, Waterbirds-100, and CelebA datasets.
  • IPG showed significant robustness to group shifts in various datasets.
  • Theoretical convergence analysis supports the reliability of IPG.
  • Reduced dependency on extensive supervision compared to existing methods.

Cite This Study

Surner et al. (2026) studied this question.

synapsesocial.com/papers/6a6850eb2845b684d16eff28https://doi.org/10.1007/s10994-026-07092-0
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Out of spuriousity: Improving robustness to spurious correlations without group annotations2024
  2. 2Rethinking Invariance Regularization in Adversarial Training to Improve Robustness-Accuracy Trade-off2024
  3. 3Improving Group Robustness on Spurious Correlation Requires Preciser Group Inference2024
  4. 4Learning Robust Classifiers with Self-Guided Spurious Correlation Mitigation2024 · 2 citations
  5. 5Learning Fair Invariant Representations under Covariate and Correlation Shifts Simultaneously2024