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
Tag: Contrastive learning
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