SAGE: Subpopulation-Aware Generative Enhancement for Mitigating Spurious Correlations

arXiv:2609.01051 · cs.LG, cs.CV · Submitted 2026-09-01 · Read on arXiv

cs.LG, cs.CV

Submitted: 2026-09-01

Updated: 2026-09-01

Code: https://github.com/luoym-lym/SAGE

Terminology

• Clustering — 26× in this paper · explained in 2 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Benchmark — 10× in this paper · explained in 2 episodes
• Fine-Tuning — 10× in this paper · explained in 5 episodes
• Robustness — 10× in this paper · explained in 5 episodes
• Data Augmentation — 8× in this paper · explained in 7 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Stable Diffusion — 7× in this paper
• Diffusion Model — 6× in this paper · explained in 8 episodes
• Embeddings — 6× in this paper · explained in 2 episodes
• Generative Model — 6× in this paper · explained in 2 episodes
• Core — 5× in this paper · explained in 2 episodes
• Diffusion Models — 5× in this paper · explained in 6 episodes
• Ablation Studies — 4× in this paper · explained in 1 episode
• Subpopulation-Aware Generative Enhancement — 4× in this paper
• Adaptation — 3× in this paper · explained in 1 episode
• Affinity Propagation — 3× in this paper
• Alignment — 3× in this paper · explained in 1 episode
• Deep Feature Reweighting — 2× in this paper
• Empirical Risk Minimization — 2× in this paper
• Generalization — 2× in this paper · explained in 13 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Low-Rank Adaptation (LoRA) — 2× in this paper · explained in 9 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Cross-Attention Mechanism — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes

Sources

• IT-OSE: Exploring Optimal Sample Size for Industrial Data Augmentation

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