I spoke about triplet loss in the previous post. I wanted to build a speaker embedding similar to the face embedding used in FaceNet. It turns out that the guys at Baidu beat me to it: https://arxiv.org/pdf/1705.02304.pdf. They have datasets with 50k speakers! And …
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The triplet loss came to my attention when looking at OpenFace. The concept of directly learning an embedding (without having to fudge things by training with softmax, then using a hidden layer as the embedding) was intriguing. They published a paper: FaceNet: A Unified Embedding for Face Recognition and Clustering …
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