![]() Transfer learning is often used as a synonym for fine-tuning, although that's not always the case. To conclude, in the context of machine learning, meta-learning usually refers to learning something that you usually don't learn in the standard problem or, as the definition of meta above suggests, to perform "higher-order" learning. If you read the paper, which I did a long time ago, you will understand that they are talking about a higher-order MDP. For example, in the paper Metacontrol for Adaptive Imagination-Based Optimization, they even formalize the concept of a meta-Markov decision process. The concept of meta-learning is also common in reinforcement learning. If you also want to learn the hyperparameters, then you will, in this sense, learn how to learn. So, in this case, training the neural network is the task of "learning". So, in this usual case, you will train a network (learn), but you will not know that the hyperparameters that you set are the most appropriate ones. the learning rate), the number of layers, etc. To do that, usually, you manually specify the optimizer, its parameters (e.g. In fact, the meaning of the word "meta" in meta-learning isĭenoting something of a higher or second-order kindįor example, in the context of training a neural network, you want to find a neural network that approximates a certain function (which is represented by the dataset). the hyperparameters), where learning is roughly a synonym for optimization. the one in the MAML paper and also described in this answer), which may not be completely consistent across sources, meta-learning is about learning to learn or learning something that you usually don't directly learn (e.g. Roughly speaking, although you can have formal definitions (e.g. In fact, I think I had already roughly read some of the cited research papers (e.g. However, in this case, although it may sound confusing to you, all of the current descriptions on this page (in your question and the other answers) don't seem inconsistent with my knowledge. For example, some may say that multi-task learning is a sub-category of transfer learning, others may not think so.įirst of all, I would like to say that it is possible that these terms are used inconsistently, given that at least transfer learning, AFAIK, is a relatively new expression, so, the general trick is to take terminology, notation and definitions with a grain of salt. I also found a similar question, but the answers seem not to agree with each other. People also use terms like "meta-transfer learning", which makes me think both types of learning have a strong connection with each other. I understand that there is a lot more to discuss, but, broadly speaking, I do not see so much difference between the two. It goes the same with transfer learning, as it may reuse partially a trained network to solve related tasks. Meta-learning is said to be "model agnostic", yet it uses metadata (hyperparameters or weights) from previously learned tasks. The comparisons still confuse me as both seem to share a lot of similarities in terms of reusability. Tasks and makes use of it to boost learning in a related target task. ![]() The same learning approach in Machine Learning, transfer learningĬomprises methods to transfer past experience of one or more source The new problem that we try to solve is similar to a few of our pastĮxperiences, it becomes easier for us. Task in the future but learning completely new tasks, too. We make use of our past experience for not only repeating the same ![]() Models are designed to accomplish a single task. ![]() In practice, most of the time, machine learning Using the experience gained by solving predecessor problems which are Transfer learning aims at improving the process of learning new tasks Make a better decision of chosen learning algorithm(s) to solve the The meta data includes properties about theĪlgorithm used, learning task itself etc. Meta learning is a part of machine learning theory in which someĪlgorithms are applied on meta data about the case to improve a I have read 2 articles on Quora and TowardDataScience. What are the differences between meta-learning and transfer learning? ![]()
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