Variational Inference of Disentangled Latent Concepts from Unlabeled Observations (1711.00848v3)

Abstract: Disentangled representations, where the higher level data generative factors are reflected in disjoint latent dimensions, offer several benefits such as ease of deriving invariant representations, transferability to other tasks, interpretability, etc. We consider the problem of unsupervised learning of disentangled representations from large pool of unlabeled observations, and propose a variational inference based approach to infer disentangled latent factors. We introduce a regularizer on the expectation of the approximate posterior over observed data that encourages the disentanglement. We also propose a new disentanglement metric which is better aligned with the qualitative disentanglement observed in the decoder's output. We empirically observe significant improvement over existing methods in terms of both disentanglement and data likelihood (reconstruction quality).

• The paper introduces DIP-VAE, a framework that regularizes latent covariance to achieve disentangled representations without significant reconstruction loss.
• Experimental results on datasets like 2D Shapes, CelebA, and 3D Chairs demonstrate superior disentanglement compared to standard VAE and β-VAE approaches.
• A new metric, the SAP score, is proposed to reliably quantify disentanglement, offering improved evaluation over previous metrics.

Variational Inference of Disentangled Latent Concepts from Unlabeled Observations

The paper "Variational Inference of Disentangled Latent Concepts from Unlabeled Observations" by Abhishek Kumar, Prasanna Sattigeri, and Avinash Balakrishnan from IBM Research AI examines the challenge of unsupervised learning to achieve disentangled representations. The problem is significant due to the numerous advantages that disentangled representations offer, including improved interpretability, transferability, and the capability for conducting interpretable interventions.

Methodological Overview

The authors propose a novel approach employing a variational inference framework designed to uncover disentangled latent factors from a set of unlabeled observations. They introduce a regularizer on the expectation of the approximate posterior that encourages disentanglement. This distinguishes their method from others, such as the $\beta$-VAE, which can suffer from a trade-off between disentanglement and reconstruction quality.

Theoretical Formulation

The paper builds on the principles of variational inference with the introduction of a regularizer that operates over the inferred latent prior. They propose and articulate a new framework, termed DIP-VAE (Disentangled Inferred Prior VAE), which explicitly targets the covariance of inferred latent variables to encourage independence. The authors explore two variants of their model, DIP-VAE-I and DIP-VAE-II, which differ in their treatment of covariance during regularization.

Contributions and Experimental Evaluation

1. Disentanglement Metric: The paper proposes a new metric, Separated Attribute Predictability (SAP) score, which correlates more reliably with qualitative disentanglement seen in decoder outputs than existing metrics like the Z-diff score.
2. Empirical Results: The authors provide strong empirical evidence across several datasets, including 2D Shapes, CelebA, and 3D Chairs. Notably, the DIP-VAE models surpass both the standard VAE and $\beta$-VAE in terms of achieving disentanglement, without sacrificing reconstruction quality as significantly as $\beta$-VAE when increasing $\beta$.
3. Numerical Analysis: The paper demonstrates that DIP-VAE achieves superior disentanglement scores with little degradation in sample quality, indicating that their approach effectively balances between the two objectives.

Implications and Future Directions

The implications of this approach are multifaceted. Practically, the ability to learn disentangled representations unsupervised can greatly enhance the applicability of generative models in real-world scenarios where labeled data is scarce or unavailable. Theoretically, the framework aligns with the broader objectives of representation learning by potentially providing more visually interpretable features.

Future directions suggested by the authors include addressing sampling biases in the generative processes and exploring the application of disentangled representations in transfer learning tasks. Moreover, the methodology could pave the way for advancements in understanding and operationalizing the independence of latent factors, offering insight into the fundamental structure of data.

Overall, the paper contributes significantly to the domain of unsupervised representation learning, offering a robust approach for variational inference that enhances the effectiveness and utility of disentangled representations.