  Deep learning has demonstrated abilities to learn complex structures, but
they can be restricted by available data. Recently, Consensus Networks (CNs)
were proposed to alleviate data sparsity by utilizing features from multiple
modalities, but they too have been limited by the size of labeled data. In this
paper, we extend CN to Transductive Consensus Networks (TCNs), suitable for
semi-supervised learning. In TCNs, different modalities of input are compressed
into latent representations, which we encourage to become indistinguishable
during iterative adversarial training. To understand TCNs two mechanisms,
consensus and classification, we put forward its three variants in ablation
studies on these mechanisms. To further investigate TCN models, we treat the
latent representations as probability distributions and measure their
similarities as the negative relative Jensen-Shannon divergences. We show that
a consensus state beneficial for classification desires a stable but imperfect
similarity between the representations. Overall, TCNs outperform or align with
the best benchmark algorithms given 20 to 200 labeled samples on the Bank
Marketing and the DementiaBank datasets.
