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SUMMARY:Learning Latent Representation: A Key to Domain Adaptation and Disentangling LOCATION:CoRE A (301) DESCRIPTION;ENCODING=QUOTED-PRINTABLE:
Abstract:
Variational autoencoders (VAEs) learn c ompressed representations of their input. This compressed representation of data contains semantic meaning and can be used in tasks such as supervised learning and reinforcement learning, transfer learning, and zero-shot lear ning. We assume that the data has been generated from a fixed number of ind ependent factors of variation and aim to learn a representation where a cha nge in one dimension corresponds to a change in one factor of variation whi le being relatively invariant to changes in other factors. I would like to present our proposed method to tackle the above problems. In our method, we learn the disentanglement representation, by choosing a latent dimension l arge enough to keep the major factors including the minor nuances/noise. By introducing relevance indicator variables, the learning loss (e.g., total correlation loss) considers relevant disentangled factors by tolerating lar ge prior divergence of these factors from those a priori specified in the n uisance model, while simultaneously attempting to identify the noise factor s with small divergence from the same nuisance priors.
For multi-sour ce data where the generating process for each source is different, a superv ised learner trained in one domain when applied to another domain will perf orm poorly. Domain adaptation (DA) addresses the above-mentioned problem by transferring knowledge from a label-rich domain (i.e., source domain) to a label-scarce domain or data from different distribution (i.e., target doma in). More specifically in the unsupervised domain adaptation, the labels fo r the target domain are not known during the learning process. In this work , we propose a way to achieve hypothesis consistency using Gaussian Process (GP). The GP allows us to induce a hypothesis space of classifiers from th e posterior distribution of the latent random functions, turning the learni ng into a, significantly easier to solve than previous approaches based on adversarial mini-max optimization.
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