Papers by Ilya Shapiro
Polarized-VAE: Proximity Based Disentangled Representation Learning for Text Generation (2021.eacl-main)
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| Challenge: | Existing methods for learning disentangled representations of real-world data focus on attribute labels or unsupervised methods that manipulate factorization in the latent space of models such as the variational autoencoder (VAE). |
| Approach: | They propose an approach that disentangles select attributes in the latent space based on proximity measures reflecting the similarity between data points with respect to these attributes. |
| Outcome: | The proposed method outperforms the VAE baseline and is competitive with state-of-the-art approaches while being more a general framework applicable to other attribute disentanglement tasks. |