Inverse Entropic Optimal Transport Solves Semi-supervised Learning via Data Likelihood Maximization
arXiv:2410.02628v5 Announce Type: replace Abstract: Learning conditional distributions $\pi^*(\cdot|x)$ is a central problem in machine learning, which is typically approached via supervised methods with paired data $(x,y) \sim \pi^*$. However, acquiring paired data samples is often challenging, especially in problems such as domain translation. This necessitates the development of $\textit{semi-supervised}$ models that utilize both limited paired data and additional unpaired i.i.d. samples $x \sim \pi^*_x$ and $y \sim \pi^*_y$ from the marginal distributions. The usage of such combined data i
The increasing availability of large unlabeled datasets alongside limited labeled data is driving research into more efficient semi-supervised learning techniques.
Improved semi-supervised learning methods address a critical bottleneck in AI development, reducing the cost and effort required for training complex models.
This research provides a more robust and theoretically sound approach to leveraging both paired and unpaired data for conditional distribution learning, potentially accelerating AI development in data-scarce domains.
- · AI researchers
- · Machine learning startups
- · Industries with limited labeled data (e.g., medical imaging, specialized manufac
- · AI model developers
- · Supervised learning-centric data labeling services (partially)
- · Less efficient semi-supervised learning approaches
More accurate and data-efficient AI models become feasible across various applications.
Reduced data acquisition and labeling costs could lower the barrier to entry for AI development in new sectors.
Accelerated deployment of AI solutions in domains previously hindered by expensive data annotation, leading to new market opportunities.
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Read at arXiv cs.LG