Adversarial encoder-multi-task-decoder for multi-stage processes

Andre Mendes, Julian Togelius, Leandro dos Santos Coelho

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    In multi-stage processes, decisions occur in an ordered sequence of stages. Early stages usually have more observations with general information (easier/cheaper to collect), while later stages have fewer observations but more specific data. This situation can be represented as a dual funnel structure, in which the sample size decreases from one stage to the other while the information available about each instance increases. Training classifiers in this scenario is challenging since information in the early stages may not contain distinct patterns to learn (underfitting). In contrast, the small sample size in later stages can cause overfitting. We address both cases by introducing a framework that combines adversarial autoencoders (AAE), multitask learning (MTL), and multi-label semi-supervised learning (MLSSL). We improve the decoder of the AAE with MTL so it can jointly reconstruct the original input and use feature nets to predict the features for the next stages. We also introduce a sequence constraint in the output of an MLSSL classifier to guarantee the sequential pattern in the predictions. Using different domains (selection process, medical diagnosis), we show that our approach outperforms other state-of-the-art methods.

    Original languageEnglish (US)
    Title of host publicationProceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Number of pages8
    ISBN (Electronic)9781728188089
    StatePublished - 2020
    Event25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, Italy
    Duration: Jan 10 2021Jan 15 2021

    Publication series

    NameProceedings - International Conference on Pattern Recognition
    ISSN (Print)1051-4651


    Conference25th International Conference on Pattern Recognition, ICPR 2020
    CityVirtual, Milan


    • Adversarial autoencoder
    • Multi-stage
    • Multi-task

    ASJC Scopus subject areas

    • Computer Vision and Pattern Recognition


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