Optimal Transport vs Total Variation Distance

Developers should learn Optimal Transport when working on machine learning tasks involving distribution alignment, such as generative models (e meets developers should learn tvd when working on tasks involving probabilistic models, such as evaluating generative models (e. Here's our take.

Optimal Transport

Nice Pick

Developers should learn Optimal Transport when working on machine learning tasks involving distribution alignment, such as generative models (e

Pros

• g
• Related to: probability-theory, machine-learning

Cons

• Specific tradeoffs depend on your use case

Total Variation Distance

Developers should learn TVD when working on tasks involving probabilistic models, such as evaluating generative models (e

Pros

• g
• Related to: probability-theory, statistical-inference

Cons

• Specific tradeoffs depend on your use case

The Verdict

Use Optimal Transport if: You want g and can live with specific tradeoffs depend on your use case.

Use Total Variation Distance if: You prioritize g over what Optimal Transport offers.

The Bottom Line

Optimal Transport wins

Developers should learn Optimal Transport when working on machine learning tasks involving distribution alignment, such as generative models (e