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So you’re setting up face recognition for yourself on your mobile device and it requires thousands of your pictures to recognize you and unlock your phone. Sounds like a technical nightmare, doesn’t it? But given the need to improve the accuracy of data models, isn’t accruing more and more data only fair? Of course, the fact that data is the life-blood of any machine learning model and ensures its success holds true. A learning model fed with sufficient and quality data is more likely to yield accurate results. But accruing a lot of data can often get unrealistic and difficult to achieve- given the high costs involved and the ability to manage data.
That’s where a learning model like few-shot learning comes into the picture.
Few-shot learning or low-shot learning refers to the practice of feeding a learning model with a very small amount of data, contrary to the normal practice of using a large amount of data. The training datasets contain very limited amounts of information. It is mostly implemented in areas where a model is expected to give appropriate results even without having several training samples, in computer vision, for instance.
A common practice in machine learning is to feed maximum data to the learning model. This is because feeding more data enables better prediction. Few-shot learning, on the other hand, aims to build accurate machine learning models with training data. It is important because it helps companies reduce cost, time, computation, data management and analysis.
Few-shot learning models are driven by the concept that reliable algorithms can be created from minimalist datasets. Here are some driving factors behind its increasing adoption:
The aim of implementing few-shot learning is to predict the correct class of instances when a small amount of information is available in the training dataset. Zero-shot learning aims to predict the correct class without being exposed to any instances belonging to that class in the training dataset at all. In zero-shot learning, a learner observes samples from classes that were not observed during training, and predicts the category they belong to. In one-shot learning, the aim is to learn information about object categories from one training sample. The learner is exposed to one instance of each class and is required to make multiple predictions based on it.
Given the minimal datasets required and low cost involved, few-shot learning has found uses across multiple areas:
It has become evident that few-shot learning in machine learning is proving to be the best-fit solution whenever training is challenged by the scarcity of data or the costs involved in training data models. A research by IBM predicts that machine learning will evolve around the following 3 major segments in the future:
We can see that machine learning has undergone enormous growth in recent years. The increase in advanced algorithms and learning models, the powerful computing capabilities of machines, and big data management have all contributed significantly to this growth. A point to note here is that we can’t claim yet that ML advancement has reached its pinnacle. We will continue to see more breakthroughs in the form of techniques, optimization and use cases. It is, therefore, in the best interest of businesses to quickly identify their “intelligent” needs and adopt relevant solutions at the earliest.
If you’re a business that’s looking for adoption of AI and ML solutions and needs guidance on various opportunities available for them in this domain, get in touch with us.