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Data Augmentation: A Fourier Analysis Perspective

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arXiv:2606. 24418v1 Announce Type: cross Abstract: Data augmentation is a simple and model-agnostic approach for exploiting known invariances in learning problems.

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Authors: Behrooz Tahmasebi, Melanie Weber, Stefanie Jegelka
Released: 2026-06-24 (15d ago)
Topic: Related to current project
Relation to my work: Read next because Data Augmentation: A Fourier Analysis Perspective overlaps with clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "A pretraining-data-poisoned Qwen3-4B backdoor only fires on the exact trigger tokens — paraphrases don't activate it, and base-model similarity to the trigger doesn't predict which inputs fire (MODERATE confidence)". Matching terms: class, under, rate, without, full, symmetry, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.24418

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