Few shot learning aims at leveraging huge database for training deep neural nets models to be used onto problems with very few data. Among other methods we will focus on metric learning algorithms because they allow for immediate adaptation of the model in production. To develop such model, fast experiment is key; we will present a versatile framework for their implementation in tf.keras.
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