Few-shot learning (FSL), also referred to as low-shot learning (LSL) in few sources, is a type of machine learning problem where the training dataset contains limited information.

A common practice for machine learning applications is to feed as much data as the model can take. This is because in most machine learning applications feeding more data enables the model to predict better. However, few-shot learning aims to build accurate machine learning models with less training data. As the dimension of input data is a factor that determines resource costs (e.g., time costs, computational costs, etc) companies can reduce data analysis/machine learning (ML) costs by using few-shot learning

Test base for learning like humans: Humans can spot the difference between handwritten characters after seeing a few examples. However, computers need large amounts of data to classify what they “see” and spot the difference between handwritten characters. Few-shot learning is a test base where computers are expected to learn from a few examples like humans.

Learning for rare cases: By using few-shot learning, machines can learn rare cases. For example, when classifying images of animals, a machine learning model trained with few-shot learning techniques can classify an image of a rare species correctly after being exposed to a small amount of prior information.

Reducing data collection effort and computational costs: As few-shot learning requires fewer data to train a model, high costs related to data collection and labeling are eliminated. A low amount of training data means low dimensionality in the training dataset, which can significantly reduce computational costs.

Computer Vision: Computer vision explores how computers can gain high-level understanding from digital images or videos. Few-shot learning is used mainly in computer vision to deal with problems

Learning for rare cases: By using few-shot learning, machines can learn rare cases. For example, when classifying images of animals, a machine learning model trained with few-shot learning techniques can classify an image of a rare species correctly after being exposed to a small amount of prior information.

Natural Language Processing: Few-shot learning enables natural language processing applications to complete tasks with few examples of text data.

Acoustic Signal Processing: F Data that contains information regarding voices/sounds can be analyzed by acoustic signal processing

Generative adversarial networks(GAN) were introduced by Ian Goodfellow in 2014 and have been a very active topic of machine learning research in recent years. GAN’s are unsupervised generative models which implicitly learn an underlying distribution. In the GAN framework, the learning process is a minimax game between two networks such as a generator and discriminator, where a generator, which generates synthetic data given a random noise vector, and a discriminator discriminates between real data and the generator’s synthetic data.

For robots to behave more like humans, they should be able to generalize information from a few demonstrations. Therefore, few-shot learning plays a critical role in training robots to complete certain tasks

They are a clever way of training a generative model by framing the problem as a supervised learning problem with two sub-models: Such as a generator model that we train to generate new examples and the discriminator model that tries to classify examples as either real (from the domain) or fake (generated). The two models are trained together in a zero-sum game, adversarial, until the discriminator model is fooled about half the time, meaning the generator model is generating plausible examples.

Getting a larger dataset is one of the most reliable ways to improve the performance of a machine learning algorithm. In some cases, adding generated or synthetic data or a process known as data augmentation can also improve performance. Data Augmentation using GANs is used for generating synthetic data for a binary classification problem. It showed that a decision tree classifier performed better when trained on this synthetic dataset than when trained on the original small dataset. However, this seems to be an exceptional case, and this straightforward approach to data augmentation has a better chance of working on very small datasets. The Effectiveness of Data Augmentation in Image Classification Using Deep Learning, the authors found that straightforward data augmentation using GANs was less effective than other augmentation strategies.

Robotics presents many unique challenges for learning algorithms. First, robots must perform a wide range of tasks, and it is often time-consuming or even infeasible to code completely new learning algorithms and features for each task. Second, robots must handle a huge amount of variety in the real world, which is difficult for many learning algorithms to handle. Finally, time is at a premium in most robotic applications, so learning algorithms must lend themselves to fast inference to be useful for robotic applications. Deep learning algorithms make them ideal choices for robotics. Modern deep learning techniques make use of unsupervised feature learning algorithms to learn good features from data to initialize the network. This allows the final back-propagation step to obtain better, more general results by starting from a good representation of the problem. These feature learning methods are particularly important in robotics, as for many robotic tasks, it is very difficult to design useful features by hand. For example, hand-designing visual features are useful for grasping. However, most other learning algorithms would require significant work to similarly reduce the feature set, since the engineer would have to test different feature sets and weigh the trade-offs each gives in terms of accuracy vs. performance. Deep learning approaches let us simply define the size of the feature set to be learned and allow the algorithm to learn an optimal task-specific feature set of that size