HyperLearn: A Distributed Approach for Representation Learning in Datasets With Many Modalities

TL;DR

HyperLearn introduces a scalable, distributed hypergraph-based framework using Graph Convolutional Networks for multimodal representation learning, efficiently handling complex intra- and inter-relations and seamlessly incorporating new modalities.

Contribution

It presents a novel distributed hypergraph and GCN-based framework that scales to many modalities and data streams, enabling efficient multimodal representation learning.

Findings

Effective in capturing complex intra- and inter-modal relations

Scales efficiently with additional modalities without increasing computational time

Demonstrated on multimedia datasets with higher-order relations

Abstract

Multimodal datasets contain an enormous amount of relational information, which grows exponentially with the introduction of new modalities. Learning representations in such a scenario is inherently complex due to the presence of multiple heterogeneous information channels. These channels can encode both (a) inter-relations between the items of different modalities and (b) intra-relations between the items of the same modality. Encoding multimedia items into a continuous low-dimensional semantic space such that both types of relations are captured and preserved is extremely challenging, especially if the goal is a unified end-to-end learning framework. The two key challenges that need to be addressed are: 1) the framework must be able to merge complex intra and inter relations without losing any valuable information and 2) the learning model should be invariant to the addition of new and potentially very different modalities. In this paper, we propose a flexible framework which can scale to data streams from many modalities. To that end we introduce a hypergraph-based model for data representation and deploy Graph Convolutional Networks to fuse relational information within and across modalities. Our approach provides an efficient solution for distributing otherwise extremely computationally expensive or even unfeasible training processes across multiple-GPUs, without any sacrifices in accuracy. Moreover, adding new modalities to our model requires only an additional GPU unit keeping the computational time unchanged, which brings representation learning to truly multimodal datasets. We demonstrate the feasibility of our approach in the experiments on multimedia datasets featuring second, third and fourth order relations.