#Maml meta learning how to#

Additionally, meta-learning has been popular in language modeling tasks, like filling in missing words and machine translation, and is also being applied to speech recognition tasks, like cross-accent adaptation. Problems-including classification, object detection and segmentation, landmark prediction, video synthesis, and others. These are also some of the reasons why meta-learning is successful in applications that require data-efficient approaches for example, robots are tasked with learning new skills in the real world, and are often faced with new environments.įurther, computer vision is one of the major areas in which meta-learning techniques have been explored to solve few-shot learning Why now?įrom a deep learning perspective, meta-learning is particularly exciting and adoptable for three reasons: the ability to learn from a handful of examples, learning or adapting to novel tasks quickly, and the capability to build more generalizable systems. This is what makes meta-learning particularly attractive. Most important, the ability to learn new tasks quickly during model inference is something that conventional machine learning approaches do not attempt. When there are not enough user reviews or ratings for obscure movies or products, it can The same could be true of recommendation engines Poor performance of models/applications based on long-tailed or imbalanced data distributions. Search engines, perhaps a few keywords are commonly searched for, whereas a vast majority of keywords are rarely searched for. In addition, certain real world problems have long-tailed and imbalanced data distributions, which may make it difficult to collect trainingĮxamples. While pre-training is beneficial, these approaches become less effective for domain-specific problems, which still require large amounts of task-specific labeled data to achieve good performance. These datasets are expensive to create, especially when one needs to involve a domain expert. Not exactly.įirst, supervised learning through deep learning methods requires massive amounts of labeled training data. Figure 1: Humans can learn things quickly Why should we care?Īn experienced ML practitioner might wonder: isn’t this covered by recent (and much-accoladed) advances in transfer learning? Well, no. In a similar fashion, meta-learning leverages previous knowledge acquired from data to solve novel tasks quickly and more efficiently. The reason humans are successful in adapting and learning quickly is that they leverage knowledge acquired from prior experience to solve novel tasks. They require vast amounts of data and compute and may yet struggle to generalize. In contrast, machines-especially deep learning algorithms-typically learn quite differently.

#Maml meta learning how to#

Our ability to learn new skills and adapt to new environments quickly (based on only a few experiences or demonstrations) is not just limited to identifying new objects, learning a new language, or figuring out how to use a new tool our capabilities are much more varied. For example, we can look at one instance of a knife and be able to discriminate all knives from other cutlery items, like spoons and forks. Humans have an innate ability to learn new skills quickly. Some light on the great work that’s been done in this area so far. With Limited Labeled Data-on active learning forĭeep neural networks, but we were both intrigued and fascinated with meta-learning as an emerging capability. We decided to focus our research report- Learning This search led us to a new paradigm: meta-learning, in which an algorithm not only learnsįrom a handful of examples, but also learns to classify novel classes during model inference. Limited number of examples available during training. In early spring of 2019, we researched approaches that would allow a machine learning practitioner to perform supervised learning with only a