Adapting deep networks to new concepts from a few examples is challenging, due to the high computational requirements of standard fine-tuning procedures.
 Most work on few-shot learning has thus focused on simple learning techniques for adaptation, such as nearest neighbours or gradient descent.
 Nonetheless, the machine learning literature contains a wealth of methods that learn non-deep models very efficiently.
 In this paper, we propose to use these fast convergent methods as the main adaptation mechanism for few-shot learning.
 The main idea is to teach a deep network to use standard machine learning tools, such as ridge regression, as part of its own internal model, enabling it to quickly adapt to novel data.
 This requires back-propagating errors through the solver steps.
 While normally the cost of the matrix operations involved in such a process would be significant, by using the Woodbury identity we can make the small number of examples work to our advantage.
 We propose both closed-form and iterative solvers, based on ridge regression and logistic regression components.
 Our methods constitute a simple and novel approach to the problem of few-shot learning and achieve performance competitive with or superior to the state of the art on three benchmarks.
We propose a meta-learning approach for few-shot classification that achieves strong performance at high-speed by back-propagating through the solution of fast solvers, such as ridge regression or logistic regression. The paper proposes an algorithm for meta-learning which amounts to fixing the features (ie all hidden layers of a deep NN), and treating each task as having its own final layer which could be a ridge regression or a logistic regression. This paper proposes a meta-learning approach for the problem of few-shot classification, they use a  method based on parametrizing the learner for each task by a closed-form solver.