Wasserstein Auto-Encoders: Difference between revisions

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= Introduction =
= Introduction =
Unsupervised representation learning from large unlabeled datasets has been an area of active research. For example, in the context of computer vision, one can leverage the practically unlimited amount of unlabeled images and videos to learn good intermediate representations, which can then be used on a variety of supervised learning tasks such as image classification.
The field of supervised representation learning has made impressive progress over the years by leveraging large labeled datasets. Recent years have seen a convergence of unsupervised probabilistic generative modeling approaches and feature learning from large datasets.


= Motivation =
= Motivation =

Revision as of 21:30, 11 March 2018

Introduction

The field of supervised representation learning has made impressive progress over the years by leveraging large labeled datasets. Recent years have seen a convergence of unsupervised probabilistic generative modeling approaches and feature learning from large datasets.

Motivation

Proposed Method

Conclusion