Memory Fusion Network for Multi-view Sequential Learning

TL;DR

The paper introduces the Memory Fusion Network (MFN), a neural architecture that models view-specific and cross-view interactions in multi-view sequential learning, achieving state-of-the-art results on benchmark datasets.

Contribution

The novel MFN architecture explicitly models both view-specific and cross-view interactions through a specialized attention mechanism and memory component.

Findings

MFN outperforms existing multi-view learning approaches.

MFN achieves new state-of-the-art results on benchmark datasets.

Extensive experiments validate the effectiveness of the proposed architecture.

Abstract

Multi-view sequential learning is a fundamental problem in machine learning dealing with multi-view sequences. In a multi-view sequence, there exists two forms of interactions between different views: view-specific interactions and cross-view interactions. In this paper, we present a new neural architecture for multi-view sequential learning called the Memory Fusion Network (MFN) that explicitly accounts for both interactions in a neural architecture and continuously models them through time. The first component of the MFN is called the System of LSTMs, where view-specific interactions are learned in isolation through assigning an LSTM function to each view. The cross-view interactions are then identified using a special attention mechanism called the Delta-memory Attention Network (DMAN) and summarized through time with a Multi-view Gated Memory. Through extensive experimentation, MFN is compared to various proposed approaches for multi-view sequential learning on multiple publicly available benchmark datasets. MFN outperforms all the existing multi-view approaches. Furthermore, MFN outperforms all current state-of-the-art models, setting new state-of-the-art results for these multi-view datasets.