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Found 3 papers, 2 papers with code
Perturbing the Gradient for Alleviating Meta Overfitting
However, there can be a number of novel solutions to this problem keeping in mind any of the two objectives to be attained, i. e. to increase diversity in the tasks and to reduce the confidence of the model for some of the tasks.
Learning to Learn with Indispensable Connections
We aim to perform two things: (a) to find a sub-network capable of more adaptive meta-learning and (b) to learn new low-level features of unseen tasks and recombine those features with the already learned features during the meta-test phase.
Adaptive Prototypical Networks
Prototypical network for Few shot learning tries to learn an embedding function in the encoder that embeds images with similar features close to one another in the embedding space.
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