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Found 3 papers, 0 papers with code
Out of Distribution Reasoning by Weakly-Supervised Disentangled Logic Variational Autoencoder
Our framework consists of three steps: partitioning data based on observed generative factors, training a VAE as a logic tensor network that satisfies disentanglement rules, and run-time OOD reasoning.
Efficient Out-of-Distribution Detection Using Latent Space of $β$-VAE for Cyber-Physical Systems
In this paper, we study this problem as a multi-labeled time series OOD detection problem over images, where the OOD is defined both sequentially across short time windows (change points) as well as across the training data distribution.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
+1
Out-of-Distribution Detection in Multi-Label Datasets using Latent Space of $β$-VAE
We use the fact that compact latent space generated by an appropriately selected $\beta$-VAE will encode the information about these factors in a few latent variables, and that can be used for computationally inexpensive detection.
