Towards an Improved Metric for Evaluating Disentangled Representations

As defined by Bengio et al. [1], representation recent scholarly reviews on the topic [8, 7]. Accordingly, learning transforms observations into a format that captures a metric designed to quantify modularity and compactness the essence of data's inherent patterns and structures. An should also assess informativeness i.e., the extent to which ideal representation should exhibit five key characteristics: (a) latent codes encapsulate information about generative factors. Disentanglement, ensuring separate encoding of interpretable When the ground truth factors of variation are identifiable, factors; (b) Informativeness, capturing the diversity of data; (c) this informativeness transforms into explicitness, denoting the Invariance, maintaining stability across changes in unrelated comprehensive representation of all recognised factors [9].