![]() The goal of the proposed model is the rapid adaptation, which means learning a new function from only a few input/output pairs for that task, using prior data from similar tasks for meta-learning. by primarily modifying the top layer weights in a feedforward model) can produce good results. If the internal representation is suitable for many tasks, simply fine-tuning the parameters slightly (e.g. This can be viewed from a feature learning standpoint as building an internal representation that is broadly suitable for many tasks. The key idea underlying this method is to train the model’s initial parameters such that the model has maximal performance on a new task after the parameters have been updated through one or more gradient steps computed with a small amount of data from that new task. The meta-learner seeks to find an initialization that is not only useful for adapting to various problems but also can be adapted quickly (in a small number of steps) and efficiently (using only a few examples). It trains for a representation that can be quickly adapted to a new task, via a few gradient steps. Like other meta-learning methods, MAML trains over a wide range of tasks. What if we directly optimized for an initial representation that can be effectively fine-tuned from a small number of examples? This is exactly the idea behind this paper, model-agnostic meta-learning (MAML). ![]() Furthermore, we unfortunately don’t have an analogous pre-training scheme for non-vision domains such as speech, language, and control. However, pre-training does not go very far, because, the last layers of the network still need to be heavily adapted to the new task, datasets that are too small, as in the few-shot setting, will still cause severe overfitting. Using this approach, neural networks can more effectively learn new image-based tasks from modestly-sized datasets. In particular, when approaching any new vision task, the well-known paradigm is to first collect labeled data for the task, acquire a network pre-trained on ImageNet classification, and then fine-tune the network on the collected data using gradient descent. The paper shows the effectiveness of the proposed algorithm in different domains, including classification, regression, and reinforcement learning problems.Īrguably, the biggest success story of transfer learning has been initializing vision network weights using pre-trained ImageNet. The primary contribution of this work is a simple model and task-agnostic algorithm for meta-learning that trains a model’s parameters such that a small number of gradient updates will lead to fast learning on a new task. ![]() It can also be used with a variety of loss functions, including differentiable supervised losses and nondifferentiable reinforcement learning objectives. by requiring a recurrent model or a Siamese network ), and it can be readily combined with fully connected, convolutional, or recurrent neural networks. Unlike prior meta-learning methods that learn an update function or learning rule, this algorithm does not expand the number of learned parameters nor place constraints on the model architecture (e.g. Our focus is on deep neural network models, but we illustrate how our approach can easily handle different architectures and different problem settings, including classification, regression, and policy gradient reinforcement learning, with minimal modification. In this work, we propose a meta-learning algorithm that is general and model-agnostic, in the sense that it can be directly applied to any learning problem and model that is trained with a gradient descent procedure. ![]() The goal of meta-learning is to train a model on a variety of learning tasks, such that it can solve new learning tasks using only a small number of training samples. Meta-learning is a subfield of machine learning where automatic learning algorithms are applied on meta-data about machine learning experiments. Learning quickly is a hallmark of human intelligence, whether it involves recognizing objects from a few examples or quickly learning new skills after just minutes of experience. ![]()
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