Glossary

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Feature Extraction(also: Feature Engineering, Representation Learning)

Feature extraction is the process of identifying and isolating measurable properties or characteristics (features) from raw data such as images, audio, or text, for use in machine learning tasks. In image processing, features may include edges, textures, colours, shapes, or…

Feature Hashing(also: Hashing Trick)

A technique used in machine learning to convert text or categorical data into fixed-length numerical feature vectors by applying a hash function. Feature hashing is particularly useful for handling high-dimensional sparse data, such as the text of bug reports or user reviews. It…

Federated Learning(also: FL)

A machine-learning approach in which a shared model is trained across many user devices without the raw training data ever leaving those devices: each device computes updates locally and sends only model parameters or gradients to a central server for aggregation. Federated…

Few-Shot Learning(also: N-Shot Learning, Low-Shot Learning)

Few-shot learning is a machine learning approach that enables AI models to learn new concepts from only a small number of examples — typically 1 to 10 — rather than the hundreds or thousands traditionally required. This is achieved through techniques like meta-learning, where…

Few-Shot Object Recognition(also: Few-Shot Recognition)

A machine learning approach in which a model learns to identify a novel object from only a handful of labelled examples (commonly one to ten) rather than the hundreds or thousands typical of conventional supervised training. Few-shot object recognition underpins teachable and…

Fine-tuning(also: Model Fine-tuning, Fine-tune, Supervised Fine-tuning)

A machine-learning technique that adapts a pre-trained foundation model - typically a large language model or vision model - to a specific task, domain, or individual user by continuing training on a smaller, targeted dataset. Fine-tuning preserves the broad capabilities of the…

Foundation Model(also: Large Pretrained Model, General-Purpose AI Model, GPAI)

A foundation model is a large AI model trained on broad, general-purpose data — typically at massive scale using self-supervised or unsupervised learning — that can be adapted (fine-tuned) for a wide range of downstream tasks. Examples include CLIP, DinoV2, GPT-4, and BLIP.…

Frame differencing(also: Temporal differencing, Background subtraction)

A computer vision technique that detects motion or changes in video by comparing consecutive frames pixel by pixel. In accessibility applications, frame differencing can identify instructor actions in presentation videos, detect gestures in sign language recognition, or track…

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