Improving speaker turn embedding by crossmodal transfer learning from face embedding
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Clustering in education, particularly in large-scale online environments like MOOCs, is essential for understanding and adapting to diverse student needs. However, the effectiveness of clustering depends on its interpretability, which becomes challenging w ...
Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...
Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive features. Previous Graph Neural Networks (GNN) require a large number of labeled ...
There is a growing recognition that electronic band structure is a local property of materials and devices, and there is steep growth in capabilities to collect the relevant data. New photon sources, from small-laboratory-based lasers to free electron lase ...
Graph machine learning offers a powerful framework with natural applications in scientific fields such as chemistry, biology and material sciences. By representing data as a graph, we encode the prior knowledge that the data is composed of a set of entitie ...
This study focuses on the protection of soft-biometric attributes related to the demographic information of individuals that can be extracted from compact representations of face images, called embeddings. We consider a state-ofthe-art technology for soft- ...
Despite the huge success of deep convolutional neural networks in face recognition (FR) tasks, current methods lack explainability for their predictions because of their ``black-box'' nature. In recent years, studies have been carried out to give an interp ...
Lensless imaging provides a large panel of benefits : cost, size, weight, etc., that are crucial for wearable application, IoT or medical devices. Such setups require the design of reconstruction algorithms to recover the image from the captured measuremen ...
State-of-the-art (SOTA) face recognition systems generally use deep convolutional neural networks (CNNs) to extract deep features, called embeddings, from face images. The face embeddings are stored in the system's database and are used for recognition of ...
We present a discriminative clustering approach in which the feature representation can be learned from data and moreover leverage labeled data. Representation learning can give a similarity-based clustering method the ability to automatically adapt to an ...