TensorFlow recently released the first version of TensorFlow Similarity, a library for similarity learning, also known as metric learning and contrastive learning. It offers SOTA algorithms for metric learning and all the necessary components to research, train, evaluate, and serve similarity-based models.
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The ability to search for related objects has many real-world applications, from finding similar-looking clothes to identifying the genre of songs currently playing, helping rescue missing pets, etc. In addition, searching for related items quickly is a vital part of many core information systems, including recommendation systems, multimedia searches and clustering pipelines.
Thanks to TensorFlow Similarity, you can now train and serve models that find similar items (such as images) in a large corpus of samples. For example, as shown below, you can train a similarity model to find and cluster similar looking images of dogs and cats from the Oxford IIT Pet Dataset by only training on a few classes.
Check out this notebook to train your own similarity model.
How is TensorFlow Similarity different?
If we consider metric learning, it is different from traditional classification, as its objective is different. The model often learns to minimise the distance between similar examples and then maximises the distance between dissimilar examples in a supervised or self-supervised manner. On the other hand, TensorFlow Similarity provides necessary losses, metrics, samplers, visualises, and indexing sub-systems to make this quick and easy.
Similarity models learn to produce embeddings that project items in a metric space, where similar objects are close together and far from dissimilar objects.
In the backdrop, many of these systems are powered by deep learning models trained using contrastive learning. It teaches the model to learn an embedding space in which similar objects are close while dissimilar ones are far apart. For example, the images belonging to the same class/genre are pulled together, while distinct classes are pushed apart from each other. (as shown in the image below)
Here’s how it works
When applying TensorFlow Similarity to an entire dataset, contrastive losses allow the model to learn how to project items into the embedding space. The distances between embeddings represent how similar the input examples are. In the end, you will have a well-clustered space where the distance between dissimilar items is large and the distance between similar items is small.
Once the model is trained, the next step involves building an index that contains the embeddings of the various items you want to make searchable. At query time, TensorFlow Similarity uses fast approximate nearest neighbour search (ANN) to retrieve the closest matching objects from the index in sub-linear time.
TensorFlow Similarity learns a ‘metric embedding space’ where the distance between ’embedded points’ is a function of a valid distance metric. Moreover, these distance metrics satisfy the triangle inequality, making the space amenable to ANN search and leading to high retrieval accuracy.
Also, other approaches like using model feature extraction require an exact nearest neighbour search to find related objects and may not be as accurate as a trained similarity model. That prevents scaling, as performing an exact search requires a quadratic time in the size of the search index. In comparison, TensorFlow Similarity’s built-in ANN indexing system, which relies on the non-metric space library (NMSLIB), makes it possible to search millions of indexed items, retrieving the top-K similar matches faster.
Besides accuracy and retrieval speed, the other major benefits of similarity models are that they allow you to add an infinite new number of classes to the index without having to retrain. Alternatively, you only need to compute the embeddings for representative items of the new classes and add them to the index.
It is particularly useful when tackling problems where different items are unknown, constantly changing, or extremely large – like enabling users to discover newly released music similar to songs they have liked in the past.
Currently, TensorFlow Similarity is still in the beta stage. It supports supervised training. However, in the coming months, it plans to support semi-supervised and self-supervised learning techniques like BYOL, SWAV and SimCLR.