Modular Representations for Weak Disentanglement
The recently introduced weakly disentangled representations proposed to relax some constraints of the previous definitions of disentanglement, in exchange for more flexibility. However, at the moment, weak disentanglement can only be achieved by increasing the amount of supervision as the number of factors of variations of the data increase. In this paper, we introduce modular representations for weak disentanglement, a novel method that allows to keep the amount of supervised information constant with respect the number of generative factors. The experiments shows that models using modular representations can increase their performance with respect to previous work without the need of additional supervision.
Code (0)
등록된 구현이 없습니다.
Tasks
DisentanglementSimilar Papers 제목 키워드 기반
Weakly Supervised Disentanglement with Guarantees
Learning disentangled representations that correspond to factors of variation in real-world data is critical to interpretable and human-controllable machine learning. Recently, concerns about the viability of learning di…
DisentanglementLeveraging Relational Information for Learning Weakly Disentangled Representations
Disentanglement is a difficult property to enforce in neural representations. This might be due, in part, to a formalization of the disentanglement problem that focuses too heavily on separating relevant factors of varia…
DisentanglementRelational ReasoningTowards an Improved Metric for Evaluating Disentangled Representations
Disentangled representation learning plays a pivotal role in making representations controllable, interpretable and transferable. Despite its significance in the domain, the quest for reliable and consistent quantitative…
DisentanglementRepresentation LearningIs Disentanglement all you need? Comparing Concept-based & Disentanglement Approaches
Concept-based explanations have emerged as a popular way of extracting human-interpretable representations from deep discriminative models. At the same time, the disentanglement learning literature has focused on extract…
AllDisentanglementOn Disentangled Representations Learned From Correlated Data
The focus of disentanglement approaches has been on identifying independent factors of variation in data. However, the causal variables underlying real-world observations are often not statistically independent. In this …
DisentanglementFairness