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Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Datasets Using Deep Learning
(Springer, 2020)
Techniques used for spatio-temporal anomaly detection in an unsupervised settings has attracted great attention in recent years. It has extensive use in a wide variety of applications such as: medical diagnosis, sensor ...
Multitype Learning via Multimodal Data Embedding
(Institute of Electrical and Electronics Engineers Inc., 2021)
This paper creates a multimodal retrieval system for image and text data in a multi-type learning approach that enables text-to-image, image-to-text, text-to-text, and image-to-image retrievals. As a practical solution, a ...
Multimodal retrieval with contrastive pretraining
(Institute of Electrical and Electronics Engineers Inc., 2021)
In this paper, we present multimodal data retrieval aided with contrastive pretraining. Our approach is to pretrain a contrastive network to assist in multimodal retrieval tasks. We work with multimodal data, which has ...