Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably)

• URL: http://arxiv.org/abs/2203.12221v1
• Date: Wed, 23 Mar 2022 06:21:53 GMT
• Title: Modality Competition: What Makes Joint Training of Multi-modal Network Fail in Deep Learning? (Provably)
• Authors: Yu Huang and Junyang Lin and Chang Zhou and Hongxia Yang and Longbo Huang
• Abstract summary: It has been observed that the best uni-modal network outperforms the jointly trained multi-modal network. This work provides a theoretical explanation for the emergence of such performance gap in neural networks for the prevalent joint training framework.
• Score: 75.38159612828362
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the remarkable success of deep multi-modal learning in practice, it has not been well-explained in theory. Recently, it has been observed that the best uni-modal network outperforms the jointly trained multi-modal network, which is counter-intuitive since multiple signals generally bring more information. This work provides a theoretical explanation for the emergence of such performance gap in neural networks for the prevalent joint training framework. Based on a simplified data distribution that captures the realistic property of multi-modal data, we prove that for the multi-modal late-fusion network with (smoothed) ReLU activation trained jointly by gradient descent, different modalities will compete with each other. The encoder networks will learn only a subset of modalities. We refer to this phenomenon as modality competition. The losing modalities, which fail to be discovered, are the origins where the sub-optimality of joint training comes from. Experimentally, we illustrate that modality competition matches the intrinsic behavior of late-fusion joint training.

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