One-shot learning is the task of learning to recognize a new object after only a single exposure to that object. This is in contrast to most machine learning tasks, which require multiple examples of each object in order to learn to recognize it. One-shot learning is difficult because it requires the learner to generalize from a single example, and most machine learning algorithms are not able to do this.
There are a few ways to approach one-shot learning. One is to use a recurrent neural network (RNN), which can learn to recognize objects from a single example by going through its hidden state. Another approach is to use a Siamese network, which consists of two identical neural networks that are trained to recognize the same objects. When presented with a new object, the Siamese network can compare the two networks’ output and use that to decide whether or not the object is recognized.
Another approach is to use a metric learning algorithm, which learns a similarity function between objects. This function can then be used to compare new objects to known objects and decide whether or not they are the same. This approach has been shown to be effective for one-shot learning tasks such as image classification and object detection.
Finally, another way to approach one-shot learning is through transfer learning. This is where a model that has already been trained on a similar task is used to solve the one-shot learning task. This can be done by either fine-tuning the existing model on the one-shot learning task or by using the model as a feature extractor, which generates features that are then used by another model to solve the task. Transfer learning has been shown to be effective for tasks such as image classification and object detection.
One-shot learning is a difficult problem because it requires the learner to generalize from a single example. However, there are a few ways to approach it, such as using a recurrent neural network, a Siamese network, or a metric learning algorithm. Additionally, transfer learning can be used to solve the problem by fine-tuning an existing model or using it as a feature extractor.
References:
https://en.wikipedia.org/wiki/One-shot_learning
https://machinelearningmastery.com/tutorial-to-implement-k-nearest-neighbors-in-python-from-scratch/
https://towardsdatascience.com/transfer-learning-from-pre-trained-models-f2393f124a0
https://www.tensorflow.org/tutorials/images/transfer_learning