Variational Transfer Learning using Cross-Domain Latent Modulation
- URL: http://arxiv.org/abs/2205.15523v2
- Date: Wed, 31 Jan 2024 05:30:22 GMT
- Title: Variational Transfer Learning using Cross-Domain Latent Modulation
- Authors: Jinyong Hou, Jeremiah D. Deng, Stephen Cranefield, Xuejie Din
- Abstract summary: We introduce a novel cross-domain latent modulation mechanism to a variational autoencoder framework so as to achieve effective transfer learning.
Deep representations of the source and target domains are first extracted by a unified inference model and aligned by employing gradient reversal.
The learned deep representations are then cross-modulated to the latent encoding of the alternative domain, where consistency constraints are also applied.
- Score: 1.9662978733004601
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: To successfully apply trained neural network models to new domains, powerful
transfer learning solutions are essential. We propose to introduce a novel
cross-domain latent modulation mechanism to a variational autoencoder framework
so as to achieve effective transfer learning. Our key idea is to procure deep
representations from one data domain and use it to influence the
reparameterization of the latent variable of another domain. Specifically, deep
representations of the source and target domains are first extracted by a
unified inference model and aligned by employing gradient reversal. The learned
deep representations are then cross-modulated to the latent encoding of the
alternative domain, where consistency constraints are also applied. In the
empirical validation that includes a number of transfer learning benchmark
tasks for unsupervised domain adaptation and image-to-image translation, our
model demonstrates competitive performance, which is also supported by evidence
obtained from visualization.
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