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Robust multimodal integration method implemented in TensorFlow
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EmbraceNet: A robust deep learning architecture for multimodal classification



EmbraceNet is a novel multimodal integration architecture for deep learning models, which provides good compatibility with any network structure, in-depth consideration of correlations between different modalities, and seamless handling of missing data. This repository contains the official TensorFlow-based implementation of the EmbraceNet model, which is explained in the following paper.

  • J.-H. Choi, J.-S. Lee. EmbraceNet: A robust deep learning architecture for multimodal classification. Information Fusion, vol. 51, pp. 259-270, Nov. 2019 [Paper] [arXiv]
  title={EmbraceNet: A robust deep learning architecture for multimodal classification},
  author={Choi, Jun-Ho and Lee, Jong-Seok},
  journal={Information Fusion},


  • Python 3.6+
  • TensorFlow 1.8+

Getting started

The implementation of the EmbraceNet model is in the embracenet/ folder. Copy the folder to your code base and import it.

from embracenet import EmbraceNet

Here is a code snippet to employ EmbraceNet.

# Create an EmbraceNet object.
embracenet = EmbraceNet(batch_size=16, embracement_size=256)

# Build a pre-processing network for each modality.
# Then, feed the output of the pre-processing network to EmbraceNet.
embracenet.add_modality(input_data=modality1, input_size=512)
embracenet.add_modality(input_data=modality2, input_size=128)

# Integrate the modality data.
embraced_output = embracenet.embrace()

# Build a post-processing network with inputting embraced_output.

Please refer to the comments in embracenet/ for more information.


An example code that employs EmbraceNet to build a classifier of Fashion MNIST is included in the examples/fashion_mnist/ folder.

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