Science Tokyo's Breakthrough: New Framework Simplifies Schrödinger Bridge Models
Researchers at Science Tokyo have made a significant breakthrough in generative diffusion models. Led by Kentaro Kaba and Professor Masayuki Ohzeki, the team has developed a new framework that reformulates Schrödinger bridge models as variational autoencoders (VAEs) with multiple latent variables.
Traditional Schrödinger bridge models offer greater flexibility than standard score-based models but are mathematically complex and expensive to train. The new method, published in Physical Review Research on September 3, 2025, addresses these challenges by reinterpreting Schrödinger bridge models as VAEs with infinitely many latent variables.
The approach reduces computational costs and prevents overfitting in generative AI models. It employs a training objective with two components: prior loss and drift matching. Interrupting the training of the encoder reduces the risk of overfitting, preserving high accuracy in Schrödinger bridge models. Notably, this method is flexible and can be applied to other probabilistic rule sets, even non-Markov processes.
The research team's innovative approach promises to make generative AI models more efficient and accurate. By reducing training costs and preventing overfitting, this method could pave the way for advancements in various fields that rely on generative models, from data analysis to machine learning.
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