Deep Distributional Sequence Embeddings Based on a Wasserstein Loss

4 Dec 2019Ahmed AbdelwahabNiels Landwehr

Deep metric learning employs deep neural networks to embed instances into a metric space such that distances between instances of the same class are small and distances between instances from different classes are large. In most existing deep metric learning techniques, the embedding of an instance is given by a feature vector produced by a deep neural network and Euclidean distance or cosine similarity defines distances between these vectors... (read more)

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