Out of Distribution Reasoning by Weakly-Supervised Disentangled Logic Variational Autoencoder
This addresses the safety of machine learning models by enabling OOD reasoning for complex, real-world datasets, representing an incremental improvement over existing methods.
The paper tackled the problem of out-of-distribution (OOD) reasoning for complex datasets with unknown generative factors, proposing a partially disentangled variational autoencoder framework that outperformed state-of-the-art methods in disentanglement and end-to-end OOD reasoning on the Carla dataset.
Out-of-distribution (OOD) detection, i.e., finding test samples derived from a different distribution than the training set, as well as reasoning about such samples (OOD reasoning), are necessary to ensure the safety of results generated by machine learning models. Recently there have been promising results for OOD detection in the latent space of variational autoencoders (VAEs). However, without disentanglement, VAEs cannot perform OOD reasoning. Disentanglement ensures a one- to-many mapping between generative factors of OOD (e.g., rain in image data) and the latent variables to which they are encoded. Although previous literature has focused on weakly-supervised disentanglement on simple datasets with known and independent generative factors. In practice, achieving full disentanglement through weak supervision is impossible for complex datasets, such as Carla, with unknown and abstract generative factors. As a result, we propose an OOD reasoning framework that learns a partially disentangled VAE to reason about complex datasets. 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. We evaluate our approach on the Carla dataset and compare the results against three state-of-the-art methods. We found that our framework outperformed these methods in terms of disentanglement and end-to-end OOD reasoning.