One-Shot Learning
One-shot learning is the ability of a model to learn a new concept from just a single example. It is particularly important in applications like face verification where collecting many examples per person is impractical.
Understanding One-Shot Learning
One-shot learning is a machine learning approach where models learn to recognize or classify new categories from just a single example, mimicking the human ability to generalize from minimal exposure. This stands in sharp contrast to traditional deep learning methods that require thousands of labeled samples per class. Siamese networks and prototypical networks are popular architectures for one-shot learning, using metric learning to compare new inputs against stored examples in an embedding space. The technique is essential in applications where collecting large datasets is impractical, such as facial recognition for access control, rare disease diagnosis in medical imaging, and signature verification in banking. One-shot learning is closely related to few-shot learning and zero-shot learning, collectively falling under the umbrella of meta-learning, where models learn how to learn efficiently from limited data.
Category
Machine Learning
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Accuracy
Accuracy is a metric that measures the proportion of correct predictions out of total predictions made by a model. While intuitive, accuracy can be misleading on imbalanced datasets where one class dominates.
Active Learning
Active learning is a machine learning approach where the model selectively queries an oracle (often a human) for labels on the most informative data points. This reduces the total amount of labeled data needed to train an accurate model.
Anomaly Detection
Anomaly detection is the identification of data points, events, or patterns that deviate significantly from expected behavior. AI-based anomaly detection is used in fraud prevention, cybersecurity, and industrial monitoring.
AutoML
Automated Machine Learning (AutoML) is the process of automating the end-to-end pipeline of applying machine learning, including feature engineering, model selection, and hyperparameter tuning. AutoML democratizes AI by reducing the expertise required.
Bagging
Bagging (Bootstrap Aggregating) is an ensemble technique that trains multiple models on random subsets of training data and combines their predictions. Random Forest is the most well-known bagging-based algorithm.
Bayesian Network
A Bayesian network is a probabilistic graphical model that represents variables and their conditional dependencies using a directed acyclic graph. It enables reasoning under uncertainty and causal inference.
Bias-Variance Tradeoff
The bias-variance tradeoff is the fundamental tension in machine learning between model simplicity (high bias) and model flexibility (high variance). Optimal models balance underfitting and overfitting to generalize well to new data.
Binary Classification
Binary classification is a supervised learning task where the model assigns inputs to one of exactly two categories. Spam detection (spam vs. not spam) and medical diagnosis (positive vs. negative) are common examples.