Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations (1705.08841v1)

Abstract: We would like to learn a representation of the data which decomposes an observation into factors of variation which we can independently control. Specifically, we want to use minimal supervision to learn a latent representation that reflects the semantics behind a specific grouping of the data, where within a group the samples share a common factor of variation. For example, consider a collection of face images grouped by identity. We wish to anchor the semantics of the grouping into a relevant and disentangled representation that we can easily exploit. However, existing deep probabilistic models often assume that the observations are independent and identically distributed. We present the Multi-Level Variational Autoencoder (ML-VAE), a new deep probabilistic model for learning a disentangled representation of a set of grouped observations. The ML-VAE separates the latent representation into semantically meaningful parts by working both at the group level and the observation level, while retaining efficient test-time inference. Quantitative and qualitative evaluations show that the ML-VAE model (i) learns a semantically meaningful disentanglement of grouped data, (ii) enables manipulation of the latent representation, and (iii) generalises to unseen groups.

• The paper introduces ML-VAE, a model that uses group-level observations to learn disentangled, semantically meaningful representations.
• It factorizes latent spaces into shared content and individual style, enabling precise manipulation of generative attributes.
• Empirical results on MNIST and MS-Celeb-1M show improved classification accuracy and robust performance over traditional VAE approaches.

Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations

The paper introduces the Multi-Level Variational Autoencoder (ML-VAE), a novel construct in the field of unsupervised representation learning. This model is designed to learn disentangled representations of data, addressing the deficiency in existing models where observations are typically assumed to be independent and identically distributed (i.i.d.). The primary innovation of ML-VAE lies in its ability to leverage minimal supervision in the form of group-level observations to decompose data into distinct and manipulable factors of variation.

Key Innovations and Methodology

The ML-VAE extends the traditional Variational Autoencoder (VAE) framework by incorporating grouping information during the learning process. Existing VAEs fail to utilize the inherent structure of grouped data, as they are predicated on the i.i.d. assumption. ML-VAE, on the other hand, is designed to factorize latent representations at both the group and individual observation levels, ensuring that specific factors of variation relevant to the group can be isolated effectively.

The model assumes latent representations composed of two variables: style and content. Here, content refers to the factor common within a group, while style varies across individual observations in the group. The method revolves around constructing a variational approximation using a product of normal densities, enhancing the inference of the shared latent variable pertinent to grouped observations. This approach permits the accumulation of evidential support from the data, refining the representation as related data points are synthesized.

Practical and Theoretical Implications

The ML-VAE has significant implications in the field of AI, specifically in simplifying and enhancing interpretability of generative models. By disentangling semantic elements such as identity in facial recognition datasets or digit labels in handwritten digit datasets, ML-VAE facilitates more intuitive manipulation and understanding of generative representations.

From a practical standpoint, such disentangled representations are invaluable for tasks that require the manipulation of specific attributes while maintaining a coherent structure in the latent space. The empirical evaluations on MNIST and MS-Celeb-1M datasets demonstrate the ML-VAE’s capability to generalize across unseen data, maintaining robustness in generating realistic variations of the observed data.

Experimental Evaluation

The paper delivers quantitative results demonstrating the superior performance of ML-VAE over standard VAEs in disentangling factors of variation. The experiments involve qualitative evaluations through operations like swapping and interpolation within the latent space, as well as quantitative measures using a classification task to gauge disentanglement effectiveness. ML-VAE's use of grouped observations for inference exhibits marked improvements in capturing the true semantic nature of the data, as evidenced by higher classification accuracies when utilizing the learned content latent space.

Future Directions

Looking ahead, the ML-VAE posits several avenues for exploration, particularly in domains where structured data representations can yield marked improvements in inference quality. Extensions of this work could focus on applying ML-VAE to complex data modalities such as text or multimodal data, where semantic disentanglement can offer profound insights and enhance the capabilities of downstream tasks such as synthesis, translation, and annotation.

In summary, the introduction of the ML-VAE model represents a valuable step forward in disentangled representation learning, offering an effective methodology to harness the structure inherent in grouped observations for more semantically meaningful generative modeling. This advancement facilitates improved interpretability in machine learning models, a vital component for the responsible deployment of AI systems across varied applications.