CASL-VAE: Learning Structured Latent Variables from Unpaired Data for Semi-supervised Clustering and Paired Sample Generation
arXiv:2607.08254v1 Announce Type: new Abstract: Quantifying variability in a target population relative to a reference population is central to many scientific and clinical problems (e.g., diseased vs. healthy). Yet, without paired data and in the presence of heterogeneous target variation, existing methods struggle to separate multiple modes of target-specific variation. We propose \textit{CASL-VAE}, a deep contrastive latent variable model that learns structured latent generative factors from unpaired data. CASL-VAE factorizes variation into continuous common latent factors shared across pop
The development of CASL-VAE reflects the ongoing push for more sophisticated AI models that can extract structured information from complex, often incomplete, datasets, driven by advancements in deep learning and computational resources.
This research provides a novel method for semi-supervised clustering and data generation from unpaired data, which is critical for scientific and clinical applications where paired datasets are scarce or impossible to obtain.
The ability to learn structured latent variables from unpaired data enables more robust analysis of population variability and the generation of synthetic paired samples, potentially accelerating research in fields like medicine and biology.
- · AI researchers
- · Biotech companies
- · Healthcare diagnostics
- · Drug discovery
- · Traditional statistical methods
- · Manual data annotation services
Improved diagnostic models differentiating disease states, even with limited paired patient data.
Reduced need for expensive and time-consuming paired data collection, accelerating research and development cycles.
New AI-driven therapies and interventions based on deeper understanding of disease heterogeneity derived from previously intractable unpaired data.
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Read at arXiv cs.LG