Published July 8, 2021 | Version Published
Conference paper Open

Converting Image Labels to Meaningful and Information-rich Embeddings

  • 1. Research Centre on Interactive Media, Smart Systems and Emerging Technologies (RISE), Nicosia, Cyprus


A challenge of the computer vision community is to understand the semantics of an image that will allow for higher quality image generation based on existing high-level features and better analysis of (semi-) labeled datasets. Categorical labels aggregate a huge amount of information into a binary value which conceals valuable high-level concepts from the Machine Learning models. Towards addressing this challenge, this paper introduces a method, called Occlusion-based Latent Representations (OLR), for converting image labels to meaningful representations that capture a significant amount of data semantics. Besides being informationrich, these representations compose a disentangled low-dimensional latent space where each image label is encoded into a separate vector. We evaluate the quality of these representations in a series of experiments whose results suggest that the proposed model can capture data concepts and discover data interrelations.


This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy.



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RISE – Research Center on Interactive Media, Smart System and Emerging Technologies 739578
European Commission