Learning multimodal representations is a fundamentally complex research problem due to the presence of multiple heterogeneous sources of information. Although the presence of multiple modalities provides additional valuable information, there are two key challenges to address when learning from multimodal data: 1) models must learn the complex intra-modal and cross-modal interactions for prediction and 2) models must be robust to unexpected missing or noisy modalities during testing. In this paper, we propose to optimize for a joint generative-discriminative objective across multimodal data and labels. We introduce a model that factorizes representations into two sets of independent factors: multimodal discriminative and modality-specific generative factors. Multimodal discriminative factors are shared across all modalities and contain joint multimodal features required for discriminative tasks such as sentiment prediction. Modality-specific generative factors are unique for each modality and contain the information required for generating data. Experimental results show that our model is able to learn meaningful multimodal representations that achieve state-of-the-art or competitive performance on six multimodal datasets. Our model demonstrates flexible generative capabilities by conditioning on independent factors and can reconstruct missing modalities without significantly impacting performance. Lastly, we interpret our factorized representations to understand the interactions that influence multimodal learning. Multimodal machine learning involves learning from data across multiple modalities . It is a challenging yet crucial research area with real-world applications in robotics BID13 , dialogue systems BID26 , intelligent tutoring systems BID24 , and healthcare diagnosis (Frantzidis et al., 2010) . At the heart of many multimodal modeling tasks lies the challenge of learning rich representations from multiple modalities. For example, analyzing multimedia content requires learning multimodal representations across the language, visual, and acoustic modalities BID9 . Although the presence of multiple modalities provides additional valuable information, there are two key challenges to address when learning from multimodal data: 1) models must learn the complex intra-modal and cross-modal interactions for prediction , and 2) trained models must be robust to unexpected missing or noisy modalities during testing BID19 .In this paper, we propose to optimize for a joint generative-discriminative objective across multimodal data and labels. The discriminative objective ensures that the representations learned are rich in intra-modal and cross-modal features useful towards predicting the label, while the generative objective allows the model to infer missing modalities at test time and deal with the presence of noisy modalities. To this end, we introduce the Multimodal Factorization Model (MFM in FIG0 ) that factorizes multimodal representations into multimodal discriminative factors and modality-specific generative factors. Multimodal discriminative factors are shared across all modalities and contain joint multimodal features required for discriminative tasks. Modality-specific generative factors are unique for each modality and contain the information required for generating each modality. We believe that factorizing multimodal representations into different explanatory factors can help each factor focus on learning from a subset of the joint information across multimodal data and labels. This method is in contrast to jointly learning a single factor that summarizes all generative and discriminative information BID37 . To sum up, MFM defines a joint distribution over multimodal data, and by the conditional independence assumptions in the assumed graphical model, both generative and discriminative aspects are taken into account. Our model design further provides interpretability of the factorized representations.Through an extensive set of experiments, we show that MFM learns improved multimodal representations with these characteristics: 1) The multimodal discriminative factors achieve state-of-the-art or competitive performance on six multimodal time series datasets. We also demonstrate that MFM can generalize by integrating it with other existing multimodal discriminative models. 2) MFM allows flexible generation concerning multimodal discriminative factors (labels) and modality-specific generative factors (styles). We further show that we can perform reconstruction of missing modalities from observed modalities without significantly impacting discriminative performance. Finally, we interpret our learned representations using information-based and gradient-based methods, allowing us to understand the contributions of individual factors towards multimodal prediction and generation. In this paper, we proposed the Multimodal Factorization Model (MFM) for multimodal representation learning. MFM factorizes the multimodal representations into two sets of independent factors: multimodal discriminative factors and modality-specific generative factors. The multimodal discriminative factor achieves state-of-the-art or competitive results on six multimodal datasets. The modalityspecific generative factors allow us to generate data based on factorized variables, account for missing modalities, and have a deeper understanding of the interactions involved in multimodal learning. Our future work will explore extensions of MFM for video generation, semi-supervised learning, and unsupervised learning. We believe that MFM sheds light on the advantages of learning factorizing multimodal representations and potentially opens up new horizons for multimodal machine learning. To simplify the proof, we first prove it for the unimodal case by considering the Wasserstein distance between P X,Y and PX ,Ŷ .
We propose a model to learn factorized multimodal representations that are discriminative, generative, and interpretable. This paper presents 'Multimodal Factorization model' that factorizes representations into shared multimodal discriminative factors and modality specific generative factors. 