Continuous-Time Meta-Learning with Forward Mode DifferentiationDownload PDF

29 Sept 2021, 00:31 (modified: 02 Mar 2022, 21:41)ICLR 2022 SpotlightReaders: Everyone
Keywords: meta-learning, few-shot learning, dynamical systems
Abstract: Drawing inspiration from gradient-based meta-learning methods with infinitely small gradient steps, we introduce Continuous-Time Meta-Learning (COMLN), a meta-learning algorithm where adaptation follows the dynamics of a gradient vector field. Specifically, representations of the inputs are meta-learned such that a task-specific linear classifier is obtained as a solution of an ordinary differential equation (ODE). Treating the learning process as an ODE offers the notable advantage that the length of the trajectory is now continuous, as opposed to a fixed and discrete number of gradient steps. As a consequence, we can optimize the amount of adaptation necessary to solve a new task using stochastic gradient descent, in addition to learning the initial conditions as is standard practice in gradient-based meta-learning. Importantly, in order to compute the exact meta-gradients required for the outer-loop updates, we devise an efficient algorithm based on forward mode differentiation, whose memory requirements do not scale with the length of the learning trajectory, thus allowing longer adaptation in constant memory. We provide analytical guarantees for the stability of COMLN, we show empirically its efficiency in terms of runtime and memory usage, and we illustrate its effectiveness on a range of few-shot image classification problems.
One-sentence Summary: COMLN is a new meta-learning algorithm, where adaptation follows a gradient flow. It enables learning the amount of adaptation using SGD. We devise a novel efficient algorithm to compute the meta-gradients of COMLN, based on forward-mode diff.
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