Hi, @CatalinaAlbornoz, @Guillermo_Alonso, thanks so much for this clever idea which combines both dataset and labels!
My understanding is as follows. Training dataset D and training labels L actually do not directly contribute to model training or parameter updating, as we don’t use them to calculate the loss function. But they participate the training in an indirect way. The only thing directly contributing to the model training is the new datapoint x, as we calculate the loss function by comparing its prediction and label. So this can be considered as a supervised learning with batch size of one. Am I correct?
One more question. Is it possible to leverage this method to address the problem raised in this post?