Adapting ciDATGAN for Enhanced Human Preference Alignment in Generated Multimodal Data

Abstract

Abstract: A fundamental problem in biomedical research is the low number of observations available, mostly due to a lack of available biosamples, prohibitive costs, or ethical reasons. Augmenting few real observations with generated in silico samples could lead to more robust analysis results and a higher reproducibility rate. Here, we propose the use of conditional single-cell generative adversarial neural networks (cscGAN) for the realistic generation of single-cell RNA-seq data. cscGAN learns non-linear gene-gene dependencies from complex, multiple cell type samples and uses this information to gener

Research Question

Can the ciDATGAN approach be adapted to improve the alignment of generated multimodal data (e.g., image-text pairs) with human preferences, as evaluated by human evaluation scores and downstream task robustness?

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