no code implementations • 6 May 2018 • Yen-Yun Yu, Shireen Y. Elhabian, Ross T. Whitaker
Semi-supervised learning (SSL) has become important in current data analysis applications, where the amount of unlabeled data is growing exponentially and user input remains limited by logistics and expense.
no code implementations • 6 Mar 2019 • Jiayun Li, Mohammad K. Ebrahimpour, Azadeh Moghtaderi, Yen-Yun Yu
Ideally, attention maps predicted by captioning models should be consistent with intrinsic attentions from visual models for any given visual concept.
no code implementations • 11 Oct 2019 • Mostafa Karimi, Gopalkrishna Veni, Yen-Yun Yu
We tackle this problem by developing a handwritten-to-machine-print conditional Generative Adversarial network (HW2MP-GAN) model that formulates handwritten recognition as a text-Image-to-text-Image translation problem where a given image, typically in an illegible form, is converted into another image, close to its machine-print form.
Generative Adversarial Network Image-to-Image Translation +1
no code implementations • 15 May 2020 • Mohammad K. Ebrahimpour, Jiayun Li, Yen-Yun Yu, Jackson L. Reese, Azadeh Moghtaderi, Ming-Hsuan Yang, David C. Noelle
The coarse functional distinction between these streams is between object recognition -- the "what" of the signal -- and extracting location related information -- the "where" of the signal.
no code implementations • 30 Aug 2022 • Junxiang Huang, Alexander Huang, Beatriz C. Guerra, Yen-Yun Yu
While much of recent study in semi-supervised learning (SSL) has achieved strong performance on single-label classification problems, an equally important yet underexplored problem is how to leverage the advantage of unlabeled data in multi-label classification tasks.
no code implementations • WNUT (ACL) 2021 • Elizabeth Soper, Stanley Fujimoto, Yen-Yun Yu
Optical character recognition (OCR) from newspaper page images is susceptible to noise due to degradation of old documents and variation in typesetting.