MUCS uses mirrored unlearning and noise-consistent skew to outperform prior TDA methods for diffusion models on three datasets.
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ARIA decomposes music training data attribution into musical aspects and supplies reliability diagnostics from similarity metrics and score matrix analysis, with validation on symbolic models using counterfactual retraining.
Generative AI needs conditional, context-specific opt-in consent at inference time rather than blanket training-time consent to handle real-world rights and usage complexities.
citing papers explorer
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Training data attribution in diffusion models via mirrored unlearning and noise-consistent skew
MUCS uses mirrored unlearning and noise-consistent skew to outperform prior TDA methods for diffusion models on three datasets.
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ARIA: A Diagnostic Framework for Music Training Data Attribution
ARIA decomposes music training data attribution into musical aspects and supplies reliability diagnostics from similarity metrics and score matrix analysis, with validation on symbolic models using counterfactual retraining.
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Yes, But Not Always. Generative AI Needs Nuanced Opt-in
Generative AI needs conditional, context-specific opt-in consent at inference time rather than blanket training-time consent to handle real-world rights and usage complexities.