Learning Disentangled Representations with Latent Variation
Predictability
- URL: http://arxiv.org/abs/2007.12885v1
- Date: Sat, 25 Jul 2020 08:54:26 GMT
- Title: Learning Disentangled Representations with Latent Variation
Predictability
- Authors: Xinqi Zhu and Chang Xu and Dacheng Tao
- Abstract summary: This paper defines the variation predictability of latent disentangled representations.
Within an adversarial generation process, we encourage variation predictability by maximizing the mutual information between latent variations and corresponding image pairs.
We develop an evaluation metric that does not rely on the ground-truth generative factors to measure the disentanglement of latent representations.
- Score: 102.4163768995288
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Latent traversal is a popular approach to visualize the disentangled latent
representations. Given a bunch of variations in a single unit of the latent
representation, it is expected that there is a change in a single factor of
variation of the data while others are fixed. However, this impressive
experimental observation is rarely explicitly encoded in the objective function
of learning disentangled representations. This paper defines the variation
predictability of latent disentangled representations. Given image pairs
generated by latent codes varying in a single dimension, this varied dimension
could be closely correlated with these image pairs if the representation is
well disentangled. Within an adversarial generation process, we encourage
variation predictability by maximizing the mutual information between latent
variations and corresponding image pairs. We further develop an evaluation
metric that does not rely on the ground-truth generative factors to measure the
disentanglement of latent representations. The proposed variation
predictability is a general constraint that is applicable to the VAE and GAN
frameworks for boosting disentanglement of latent representations. Experiments
show that the proposed variation predictability correlates well with existing
ground-truth-required metrics and the proposed algorithm is effective for
disentanglement learning.
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