VIABLE: Fast Adaptation via Backpropagating Learned Loss

29 Nov 2019Leo FengLuisa ZintgrafBei PengShimon Whiteson

In few-shot learning, typically, the loss function which is applied at test time is the one we are ultimately interested in minimising, such as the mean-squared-error loss for a regression problem. However, given that we have few samples at test time, we argue that the loss function that we are interested in minimising is not necessarily the loss function most suitable for computing gradients in a few-shot setting... (read more)

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