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Out-of-distribution detection using normalizing flows on the data manifold
This study investigates the effect of manifold learning using normalizing flows on out-of-distribution detection.
Revealing Model Biases: Assessing Deep Neural Networks via Recovered Sample Analysis
While a generalization test is one way to evaluate a trained model's performance, it can be costly and may not cover all scenarios to ensure that the model has learned the primary concepts.
Joint Manifold Learning and Density Estimation Using Normalizing Flows
We propose a single-step method for joint manifold learning and density estimation by disentangling the transformed space obtained by normalizing flows to manifold and off-manifold parts.
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