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.
Questions tagged [model-agnostic-meta-learning]
9 questions
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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 has done research on the topic and you know of…
profPlum
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2
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1 answer
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-learning.html.
Specifically, could someone help to…
Blake Camp
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vote
1 answer
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 inner SGD.
I will calculate adapted parameters…
Grumpy C
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1 answer
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 train such models.
For example, let's consider we…
razvanc92
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vote
1 answer
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 but got stuck along the way (please refer to the…
Long
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0
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Which meta-learning algorithms are well-suited for "many-shot learning" scenarios, where the target training set is large?
Much of the meta-learning literature deals with the few-shot learning problem of using data from a diverse set of "source" tasks (the meta-dataset) in order to train a model that can quickly learn how to solve a new, previously unseen "target" task.…
Ori
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In MAML for RL, are new tasks sampled for every meta update, or is the same set of tasks used throughout?
Consider Model Agnostic Meta-Learning, as described here.
For a RL task $T_i$, represented with a model $f$, with parameters $\theta$ and learning rate $\alpha$, where the RL loss function is $\mathcal{L}$:
$$
\theta'_{i} = \theta -…
hazrmard
- 101
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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 learning.
This is slightly confusing to me because it…
profPlum
- 360
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0
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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.
How do I decide which approach to select for a…
Rambo_john
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