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Found 4 papers, 1 papers with code
A Metric for Linear Symmetry-Based Disentanglement
We propose a metric for the evaluation of the level of LSBD that a data representation achieves.
Quantifying and Learning Linear Symmetry-Based Disentanglement
The definition of Linear Symmetry-Based Disentanglement (LSBD) formalizes the notion of linearly disentangled representations, but there is currently no metric to quantify LSBD.
Quantifying and Learning Disentangled Representations with Limited Supervision
Although several works focus on learning LSBD representations, such methods require supervision on the underlying transformations for the entire dataset, and cannot deal with unlabeled data.
Anomaly Detection for imbalanced datasets with Deep Generative Models
We train a generative model without supervision on the `negative' (common) datapoints and use this model to estimate the likelihood of unseen data.
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