![]() 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. 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
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