Joint Multimodal Learning with Deep Generative Models (1611.01891v1)

Abstract: We investigate deep generative models that can exchange multiple modalities bi-directionally, e.g., generating images from corresponding texts and vice versa. Recently, some studies handle multiple modalities on deep generative models, such as variational autoencoders (VAEs). However, these models typically assume that modalities are forced to have a conditioned relation, i.e., we can only generate modalities in one direction. To achieve our objective, we should extract a joint representation that captures high-level concepts among all modalities and through which we can exchange them bi-directionally. As described herein, we propose a joint multimodal variational autoencoder (JMVAE), in which all modalities are independently conditioned on joint representation. In other words, it models a joint distribution of modalities. Furthermore, to be able to generate missing modalities from the remaining modalities properly, we develop an additional method, JMVAE-kl, that is trained by reducing the divergence between JMVAE's encoder and prepared networks of respective modalities. Our experiments show that our proposed method can obtain appropriate joint representation from multiple modalities and that it can generate and reconstruct them more properly than conventional VAEs. We further demonstrate that JMVAE can generate multiple modalities bi-directionally.

• The paper introduces the JMVAE model that jointly learns shared representations across modalities for effective bidirectional generation.
• It employs a divergence reduction technique (JMVAE-kl) that enhances handling of missing modalities with improved log-likelihood scores.
• The approach offers practical benefits in applications like image captioning and text-to-image synthesis, paving the way for future research.

Joint Multimodal Learning with Deep Generative Models

This paper addresses a significant challenge in the domain of deep generative models: the ability to handle multiple modalities bi-directionally in a coherent learning framework. Traditionally, models like variational autoencoders (VAEs) primarily focus on single-directional conditional generation. The authors propose an innovative model, the Joint Multimodal Variational Autoencoder (JMVAE), to overcome this limitation. This model enables the generation of images from texts and vice versa by extracting a joint representation that captures high-level concepts across different modalities.

Methodological Contributions

The JMVAE stands out by modeling a joint distribution of modalities, allowing for the simultaneous conditioning on a latent variable $\mathbf{z}$. The authors supplement this with the introduction of JMVAE-kl, a method designed to address the challenge of generating missing modalities from available modalities effectively. This is achieved by reducing divergence among encoders for each respective modality, a strategy that enhances the robustness of bidirectional modality generation.

Evaluation and Results

The empirical evaluation of JMVAE on datasets such as MNIST and CelebA demonstrates its superiority in bi-directional generation compared to standard VAEs and CVAEs. Notably, the JMVAE-kl variant shows improved performance over JMVAE-zero when dealing with missing modalities, suggesting the effectiveness of the divergence reduction approach. The paper reports strong quantitative results, with the JMVAE consistently achieving higher log-likelihoods, indicating that it captures joint representations more effectively than previous models. This performance is further validated through qualitative assessments, showcasing the model’s ability to generate diverse outputs conditioned on varying inputs.

Practical and Theoretical Implications

From a practical standpoint, the ability of JMVAEs to handle multimodal data bi-directionally can revolutionize applications in areas such as image captioning, text-to-image synthesis, and more. They provide a means to leverage associations between modalities effectively, improving the versatility and applicability of generative models in real-world scenarios.

Theoretically, this work prompts reevaluation of the standard approaches to multimodal learning with generative models. By demonstrating that a joint representation can facilitate robust bi-directional generation, it paves the way for further explorations into joint distribution learning within other deep learning frameworks.

Future Directions

Looking forward, the exploration of JMVAE across modalities beyond images and text is a promising research direction. The scalability and adaptability of this approach to encompass more complex, higher-dimensional datasets remain open questions. Additionally, integrating the benefits of adversarial training, as initiated with JMVAE-GAN, could continue to improve the quality of generative outputs, deserving further investigation.

In conclusion, this paper makes a significant methodological contribution to the field of multimodal deep generative models, offering an effective solution to the challenge of bi-directional generation through a joint representational approach. The proposed framework serves as a foundational basis for future developments in this evolving area of research.