X-CNN: Cross-modal Convolutional Neural Networks for Sparse Datasets

TL;DR

This paper introduces X-CNNs, a biologically inspired neural network architecture that enhances learning from sparse datasets by enabling information exchange between specialized subnetworks, outperforming traditional CNNs in low-data scenarios.

Contribution

The paper proposes a novel cross-modal CNN architecture that allows for unconstrained information flow between subnetworks, improving performance on sparse datasets.

Findings

X-CNNs outperform standard CNNs on CIFAR-10 and CIFAR-100 with limited training data.

Cross-modal architecture provides a 2-6% accuracy improvement in low-data environments.

X-CNNs maintain competitive performance even with full datasets.

Abstract

In this paper we propose cross-modal convolutional neural networks (X-CNNs), a novel biologically inspired type of CNN architectures, treating gradient descent-specialised CNNs as individual units of processing in a larger-scale network topology, while allowing for unconstrained information flow and/or weight sharing between analogous hidden layers of the network---thus generalising the already well-established concept of neural network ensembles (where information typically may flow only between the output layers of the individual networks). The constituent networks are individually designed to learn the output function on their own subset of the input data, after which cross-connections between them are introduced after each pooling operation to periodically allow for information exchange between them. This injection of knowledge into a model (by prior partition of the input data through domain knowledge or unsupervised methods) is expected to yield greatest returns in sparse data environments, which are typically less suitable for training CNNs. For evaluation purposes, we have compared a standard four-layer CNN as well as a sophisticated FitNet4 architecture against their cross-modal variants on the CIFAR-10 and CIFAR-100 datasets with differing percentages of the training data being removed, and find that at lower levels of data availability, the X-CNNs significantly outperform their baselines (typically providing a 2--6% benefit, depending on the dataset size and whether data augmentation is used), while still maintaining an edge on all of the full dataset tests.