Congratulations to Dr. Aditya Dutt for successfully passing his PhD dissertation exam! Dr. Dutt’s research introduced the Contrastive MultiModal Alignment Network (COMMANet), a novel approach to shared manifold-based domain translation and fusion. His work addressed the challenge of limited and… Read More
Tag: Multimodal triplet autoencoder
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