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Sequence Analysis and Modeling Lab

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Category: Journal Paper

Posted on September 22, 2019October 2, 2019

Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach

This is a joint work by Behnam Gholami, Pritish Sahu, Ognjen Rudovic, Konstantinos Bousmalis, Vladimir Pavlovic

Abstract

Unsupervised domain adaptation (uDA) models focus on pairwise adaptation settings where there is a single, labeled, source and a single target domain. However, in many real-world settings … Read the rest

Posted on September 29, 2017

Chapter on “Machine Learning Methods for Social Signal Procesing” in Social Signal Processing book

Social Signal Processing book by Cambridge University Press was finally published this July.   Read our chapter on “Machine Learning Methods for Social Signal Processing”[1] on p. 234 of this collection of outstanding articles.

References

[1] O. Rudovic, M.
… Read the rest
Posted on June 23, 2017July 18, 2017

Depth Recovery Paper

Our article on depth recovery using deformable object priors was accepted for publication in Journal of Visual Communication and Image Representation [1]:

[1] C. Chen, H. X. Pham, V. Pavlovic, J. Cai, G. Shi, and Y. Gao, “Using
… Read the rest
Posted on November 29, 2016July 18, 2017

Ancient Coin Recognition Paper

 Our article dealing with the problem of Ancient Coin Recognition was recently accepted for publication [1]:

[1] [doi] J. Kim and V. Pavlovic, “Discovering Characteristic Landmarks on Ancient Coins Using Convolutional Networks,” SPIE Journal of Electronic Imaging, 2017.
… Read the rest

Recent Posts

  • Spring 2022 – CS536 – Machine Learning
  • Fall 2021 Courses – CS535 Pattern Recognition
  • “These Pizzas Do Not Exist”
  • Picture-to-Amount (PITA): Predicting Relative Ingredient Amounts from Food Images
  • New NSF Grant: Learning Joint Crowd-Space Embeddings for Cross-Modal Crowd Behavior Prediction

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    SEQAM Lab
    CBIM Center
    Rutgers University
    617 Bowser Road
    Piscataway, New Jersey 08854
    United States of America
    Phone: +1 (848) 445-8846
    Fax:  +1 (732) 445-0537

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