Tag: COMMANet

Shared Manifold Learning Using a Triplet Network for Multiple Sensor Translation and Fusion with Missing Data

Abstract: Heterogeneous data fusion can enhance the robustness and accuracy of an algorithm on a given task. However, due to the difference in various modalities, aligning the sensors and embedding their information into discriminative and compact representations is challenging. In… Read More

Congratulation to Aditya Dutt for publishing his new paper: Contrastive learning based MultiModal Alignment Network

Congratulations to our labmates and collaborators: Aditya Dutt, Alina Zare, and Paul Gader! Their paper, “Shared Manifold Learning Using a Triplet Network for Multiple Sensor Translation and Fusion with Missing Data”, was recently accepted to IEEE Journal of Selected Topics… Read More