Questions tagged [model-agnostic-meta-learning]

For questions related to Model-Agnostic Meta-Learning (MAML), proposed in "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks" by Chelsea Finn et al.

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How does MAML inner loop optimization works?

I started to learn meta-learning, reading the MAML paper https://arxiv.org/pdf/1703.03400.pdf In the inner loop, I am calculating adapted parameters for each task, I will be doing multiple steps of ...
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What are practical methods to acquire a large number of tasks for Meta-learning?

It appears that it may be necessary to acquire a very large number of tasks for meta-learning , because MAML for example says that each task is analogous to a single training example in regular ...
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How many tasks are needed for meta-learning?

This is an empirical question, essentially how many tasks do you need data for, to make a useful meta learning model (e.g. using MAML)? I'm looking for ranges based on personal experience or if anyone ...
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How to split data for meta-learning?

I've been trying to understand the meta-learning paradigm, more precisely, the optimization-based models, such as MAML, but I have a hard time understanding how I should correctly split my data to ...
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Which meta-learning approach selection methodology should I use for similarity learning of an image?

Meta-learning has 3 broad approaches: model, metric and optimization-based approach. Each of them has its own sub-approach, like matching network, meta-agonistic and Siamese-based network, and so on. ...
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What is $ \nabla_{\theta_{k-1}} \theta_{k}$ in the context of MAML?

I am attempting to fully understand the explicit derivation and computation of the Hessian and how it is used in MAML. I came across this blog: https://lilianweng.github.io/lil-log/2018/11/30/meta-...
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Understanding the derivation of the first-order model-agnostic meta-learning

According to the authors of this paper, to improve the performance, they decided to drop backward pass and using a first-order approximation I found a blog which discussed how to derive the math ...
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