  We use the EMNIST dataset of handwritten digits to test a simple approach for
few-shot learning. A fully connected neural network is pre-trained with a
subset of the 10 digits and used for few-shot learning with untrained digits.
Two basic ideas are introduced: during few-shot learning the learning of the
first layer is disabled, and for every shot a previously unknown digit is used
together with four previously trained digits for the gradient descend, until a
predefined threshold condition is fulfilled. This way we reach about 90%
accuracy after 10 shots.
