A Siamese network, as the name suggests, is an architecture with two parallel layers. In this architecture, instead of a model learning to classify its inputs using classification loss functions, the model learns to differentiate between two given inputs. It compares two inputs based on a similarity metric and checks whether they are the same. Similar to any deep learning architecture, a Siamese network also has two phases—a training and a testing phase. But, for a one-shot learning approach (as we won't have a lot of data points), we will be training the model architecture on one dataset and testing it on a different dataset. To put this in simpler terms, we learn image embeddings using a supervised metric-based approach with Siamese neural networks, and then reuse that network's features for one-shot learning without fine-tuning...
Hands-On One-shot Learning with Python
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Hands-On One-shot Learning with Python
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Overview of this book
One-shot learning has been an active field of research for scientists trying to develop a cognitive machine that mimics human learning. With this book, you'll explore key approaches to one-shot learning, such as metrics-based, model-based, and optimization-based techniques, all with the help of practical examples.
Hands-On One-shot Learning with Python will guide you through the exploration and design of deep learning models that can obtain information about an object from one or just a few training samples. The book begins with an overview of deep learning and one-shot learning and then introduces you to the different methods you can use to achieve it, such as deep learning architectures and probabilistic models. Once you've got to grips with the core principles, you'll explore real-world examples and implementations of one-shot learning using PyTorch 1.x on datasets such as Omniglot and MiniImageNet. Finally, you'll explore generative modeling-based methods and discover the key considerations for building systems that exhibit human-level intelligence.
By the end of this book, you'll be well-versed with the different one- and few-shot learning methods and be able to use them to build your own deep learning models.
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Hands-On One-shot Learning with Python
Table of Contents (11 chapters)
Preface
Section 1: One-shot Learning Introduction
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Introduction to One-shot Learning
Section 2: Deep Learning Architectures
Metrics-Based Methods
Model-Based Methods
Optimization-Based Methods
Section 3: Other Methods and Conclusion
Generative Modeling-Based Methods
Conclusions and Other Approaches
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