ActFlow expands the generable set of pre-trained flow models for out-of-distribution molecular and sequence design via active synthetic data generation and verifier feedback, with new statistical guarantees.
Self-improving diffusion models with synthetic data.arXiv preprint arXiv:2408.16333, 2024
3 Pith papers cite this work. Polarity classification is still indexing.
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ReSAGE-PAR adapts diffusion models with LoRA, scores generated images via vision-language prompts, and applies Bayesian classification to produce pseudo-labels, yielding up to 8.7% gains when used to expand PAR datasets.
VAEs generate synthetic malware to augment datasets, yielding reported gains in accuracy, precision, recall, and F1 for three ML classifiers.
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Enhancing Malware Detection with Generative AI: Using Variational Autoencoders to Boost Machine Learning Classifiers' Performance
VAEs generate synthetic malware to augment datasets, yielding reported gains in accuracy, precision, recall, and F1 for three ML classifiers.