Disentangling factors of variation in deep representations using adversarial training (1611.03383v1)

Abstract: We introduce a conditional generative model for learning to disentangle the hidden factors of variation within a set of labeled observations, and separate them into complementary codes. One code summarizes the specified factors of variation associated with the labels. The other summarizes the remaining unspecified variability. During training, the only available source of supervision comes from our ability to distinguish among different observations belonging to the same class. Examples of such observations include images of a set of labeled objects captured at different viewpoints, or recordings of set of speakers dictating multiple phrases. In both instances, the intra-class diversity is the source of the unspecified factors of variation: each object is observed at multiple viewpoints, and each speaker dictates multiple phrases. Learning to disentangle the specified factors from the unspecified ones becomes easier when strong supervision is possible. Suppose that during training, we have access to pairs of images, where each pair shows two different objects captured from the same viewpoint. This source of alignment allows us to solve our task using existing methods. However, labels for the unspecified factors are usually unavailable in realistic scenarios where data acquisition is not strictly controlled. We address the problem of disentanglement in this more general setting by combining deep convolutional autoencoders with a form of adversarial training. Both factors of variation are implicitly captured in the organization of the learned embedding space, and can be used for solving single-image analogies. Experimental results on synthetic and real datasets show that the proposed method is capable of generalizing to unseen classes and intra-class variabilities.

• The paper introduces a model that disentangles specified labels from latent variability using a combined VAE-GAN approach and adversarial regularization.
• It leverages a conditional architecture and joint training that swaps components to ensure robust disentanglement across diverse datasets like MNIST and NORB.
• Experimental results demonstrate high predictive power of the specified components, enhancing transfer learning and generative performance on unseen classes.

Disentangling Factors of Variation in Deep Representations Using Adversarial Training

The paper "Disentangling Factors of Variation in Deep Representations Using Adversarial Training" presents an approach designed to effectively separate different sources of variability in observational datasets through a conditional generative model. By leveraging deep convolutional autoencoders in conjunction with adversarial training, this work introduces a method that addresses the challenge of disentangling specified factors from unspecified factors of variation, even when strong supervision is not available.

Methodology

The paper introduces a novel generative model that utilizes a combination of Variational Auto-Encoders (VAEs) and Generative Adversarial Networks (GANs). The proposed model reduces reliance on labels for unspecified factors, often unavailable in uncontrolled settings.

Key Components of the Methodology:

• Conditional Model Design: The model separates observed variables into specified and unspecified components. The specified component is derived from labels, while the unspecified component captures other variability.
• Adversarial Regularization: Through adversarial training, the discriminator ensures that swapped component observations retain class identity, fostering disentanglement without needing explicit labels for the unspecified factors.
• Joint Training Procedure: The training includes variations where the same class's observations can have their components swapped and reconstructed, enhancing the generalization ability of the framework.

Experimental Results

The model is tested across diverse datasets including MNIST, Sprites, NORB, and Extended-YaleB. The experiments highlight the method's capability in generating perceptually meaningful representations by disentangling identity from style or viewing angle, as demonstrated in both synthetic and real-world datasets.

Notable Findings:

• Generalization to Unseen Classes: The method is shown to generalize effectively to classes not seen during training, especially in the Sprites dataset.
• Image Analogy Tasks: The swapping and interpolation tasks demonstrate the model’s competency in disentangling specified factors from other variations.
• Quantitative Classification Tasks: Evaluation of disentangled representations via classification tasks confirms successful parameterization, as the specified component exhibits high predictive power related to class labels.

Implications and Future Directions

The disentanglement approach holds significant potential for diversified applications, including computer vision, speech synthesis, and any domain where understanding latent factors is critical. Establishing representations that separate critical factors without discarding others can enhance downstream tasks, such as transfer learning and generative model applications.

Future exploration could focus on refining techniques to improve the quality of generated outputs, particularly in complex datasets like NORB where high variability might challenge the current model design. Additionally, investigating more nuanced disentanglement within both specified and unspecified components could unlock broader applications and enhance model interpretability.

Overall, this research paves the way for more flexible generative models capable of operating under less controlled conditions, thereby broadening the practicality of advanced representation learning in real-world scenarios.