Maml meta learning12/14/2023 A learning algorithm may perform very well in one domain, but not on the next. This means that it will only learn well if the bias matches the learning problem. įlexibility is important because each learning algorithm is based on a set of assumptions about the data, its inductive bias. As of 2017, the term had not found a standard interpretation, however the main goal is to use such metadata to understand how automatic learning can become flexible in solving learning problems, hence to improve the performance of existing learning algorithms or to learn (induce) the learning algorithm itself, hence the alternative term learning to learn. The LSTM has learned the best rule to update these parameters for an unseen test task, from experience with a series of training tasks.Is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. b) Learning to optimize setup of Ravi & Larochelle (2016). The loss and its gradient are now passed to an LSTM containing the parameters as the cell state. The parameters $\boldsymbol\phi$ of this general model can be adapted to the $j^$ with respect to the parameters and these are used to update these parameters for the next iteration. Model agnostic meta-learning or MAML (Finn et al. 2017) is a meta-learning framework that can be applied to any model that is trained with a gradient descent procedure. The aim is to learn a general model that can easily be fine-tuned for many different tasks, even when the training data is scarce. Learning to initializeĪlgorithms in this class aim to choose a set of parameters that can be fine-tuned very easily to another task via one or more gradient learning steps. This criterion encourages the network to learn a stable feature set that is applicable to many different domains, with a set of parameters on top of these that can be easily modified to exploit this representation. In the second approach (“learning to optimize”), the optimization scheme becomes the focus of learning. We constrain the optimization algorithm to produce only models that generalize well from small datasets. Finally, the third approach (“sequence methods”) learns models that treat the data/label pairs as a temporal sequence and that learns an algorithm that takes this sequence and predicts missing labels from new data. In this section we’ll discuss three related methods that are superior for the few-shot scenario. In the first approach (“learning to initialize”), we explicitly learn networks with parameters that can be fine-tuned with a few examples and still generalize well. We might either (i) fine-tune this network using the few-shot data, or (ii) use the hidden layers as input for a new classifier trained with the few-shot data. Unfortunately, when training data is really sparse, the resulting classifier typically fails to generalize well. Then we adapt this network for the few-shot task. Perhaps the most obvious approach to few-shot learning would be transfer learning we first find a similar task for which there is plentiful data and train a network for this. In part II we will discuss the remaining two families that exploit prior knowledge about learning, and prior knowledge about the data respectively. We also discussed the family of methods that exploit prior knowledge of class similarity. Prior knowledge of data: We exploit prior knowledge about the structure and variability of the data and this allows us to learn viable models from few examples. Prior knowledge about learning: We use prior knowledge to constrain the learning algorithm to choose parameters that generalize well from few examples. Prior knowledge about class similarity: We learn embeddings from training tasks that allow us to easily separate unseen classes with few examples. In part I of this tutorial we argued that few-shot learning can be made tractable by incorporating prior knowledge, and that this prior knowledge can be divided into three groups:
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