Learning Disentangled Semantic Spaces of Explanations via Invertible Neural Networks (2024.acl-long)
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| Challenge: | Disentangled latent spaces usually have better semantic separability and geometrical properties, which leads to better interpretability and controllable data generation. |
| Approach: | They propose a flow-based invertible neural network mechanism integrated with a transformer-based language Autoencoder to deliver latent spaces with better semantic separability and geometrical properties. |
| Outcome: | The proposed model can deliver latent spaces with better separability properties compared to the current state-of-the-art models. |
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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). |
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