The analogy to deep learning is that the rocket engine is the deep learning models and the fuel is the huge amounts of data we can feed to these algorithms. — Andrew Ng

One of the most important things lacking in the current state of art Deep Learning, a thing which a 3-year-old child has but not a neural network with billions of parameters trained using 100s of GPU, is the ability to learn with limited examples. With the DL research being spearheaded by Big Tech companies that have exabytes of data, the problem of learning with limited data never got prominence. Thus techniques like one shot or zero-shot and low-shot learning have not received as much attention as they ought to. These do not fit the data-hungry stereotype of Deep Learning and can be thought of as an extreme form of transfer learning.

However, lately, there has been some development in this direction with people trying to focus on problems where data is not available and cannot be easily created.

Zero-Shot Learning:

Zero-shot learning is being able to solve a task despite not having received any training examples of that task.

Consider the problem of having a learner read a large collection of text and then solve object recognition problems. It may be possible to recognize a specific object class even without having seen an image of that object if the text describes the object well enough. For example, having read that a cat has four legs and pointy ears, the learner might be able to guess that an image is a cat without having seen a cat before. (Examples shamelessly copied from – [1])

In zero-shot learning setting test and training, class sets are completely separate. This can be solved by solving related sub-problems, e.g. learning intermediate attribute classifiers and learning a mixture of seen class proportions. Zero-shot learning is only possible because additional information has been exploited during training.

Zero shot approaches are still in infancy and are an active topic for research.

One Shot (Low shot) learning:

Low-shot learning is solving a task with only handful of examples.

For one-shot learning, the trick lies in getting the network to learn representation which is not only useful in getting good classification accuracy but also discover features that relate to the underlying causes that generate the observed data.

1. Squared Gradient Magnitude loss [2] - This is a loss function which helps in regularisation of features itself rather than weights. It also tries to mimic the classifier trained on a larger data on a smaller data.

2. Feature Regularisation:

L2 Regularization of feature representations rather than weight vector (commonly used in unsupervised Learning)

Dropout

Multiverse Loss [5]

3. Metric learning based approach like triplet loss - Triplet networks and triplet loss are able to develop much richer features compared to many other image classification networks.

The process of getting One-shot learning to work is something like - During representation learning (training phase one), the learner receives a fixed set of base categories, and a dataset containing a large number of examples for each category. The learner uses the dataset to set the parameters of its feature extractor.

In the second phase - low-shot learning phase, the learner is given a set of categories that it must learn to distinguish. A mix of base categories and unseen novel categories is created. For each novel category, the learner has access to only n positive examples, where n ∈ {1, 2, 5, 10, 20}. For the base categories, the learner still has access to the original database. The learner may then use these examples and its feature extractor to set the parameters of its multi-class classifier while also optionally modifying the feature extractor. [2]

Both the above-described techniques take Deep Learning a step closer to solving the ultimate problem – understanding the underlying nature of data. The better we get at it, the less data we will require and do more abstraction and generalization.

References –

1. http://www.deeplearningbook.org/contents/representation.html

2. https://research.fb.com/publications/low-shot-visual-recognition-by-shrinking-and-hallucinating-features/

3. https://arxiv.org/abs/1301.3666

4. http://www.cs.cmu.edu/%7Efmri/papers/zero-shot-learning.pdf

5. https://arxiv.org/abs/1511.09033



