# Meta-Learning with Implicit Gradients

### Aravind Rajeswaran*, Chelsea Finn*, Sham Kakade, Sergey Levine

University of Washington, Stanford University, UC Berkeley

**Paper**: http://arxiv.org/abs/1909.04630

**Poster: **https://homes.cs.washington.edu/~aravraj/assets/research/imaml_poster.pdf

**Code**: https://github.com/aravindr93/imaml_dev (simplified version largely for conceptual clarity)

To compute the meta-gradient, the MAML algorithm differentiates through the optimization path, as shown in green, while first-order MAML computes the meta-gradient by approximating the derivate at Φ as the derivative at θ (i.e. approximating the Jacobian of Φ with respect to θ as the identity). Our implicit MAML approach derives an analytic expression for the exact meta-gradient without differentiating through the optimization path by estimating local curvature.

**Abstract: **A core capability of intelligent systems is the ability to quickly learn new tasks by drawing on prior experience. Gradient (or optimization) based meta-learning has recently emerged as an effective approach for few-shot learning. In this formulation, meta-parameters are learned in the outer loop, while task-specific models are learned in the inner-loop, by using only a small amount of data from the current task. A key challenge in scaling these approaches is the need to differentiate through the inner loop learning process, which can impose considerable computational and memory burdens. By drawing upon implicit differentiation, we develop the implicit MAML algorithm, which depends only on the solution to the inner level optimization and not the path taken by the inner loop optimizer. This effectively decouples the meta-gradient computation from the choice of inner loop optimizer. As a result, our approach is agnostic to the choice of inner loop optimizer and can gracefully handle many gradient steps without vanishing gradients or memory constraints. Theoretically, we prove that up to small constant factors, our method has a memory footprint no more than that which is required to compute a single inner loop gradient and at no overall increase in the total computational cost. Experimentally, we show that these benefits of implicit MAML translate into empirical gains on few-shot image recognition benchmarks.