Techniques

Contrastive Learning

Définition

A self-supervised technique that trains a model by pulling representations of similar samples together and pushing dissimilar ones apart. Contrastive learning produces high-quality embeddings for images and text and underpins models like CLIP, which links vision and language.

Termes Connexes

Self-Supervised Learning

A training paradigm where a model generates its own supervision signal from unlabelled data—for example, by predicting masked words in a sentence. Self-supervised pre-training on internet-scale text or images is what enables foundation models to acquire broad world knowledge.

Embedding

A numerical representation of data (text, images, etc.) in a high-dimensional vector space where semantically similar items are mathematically close together. Embeddings are the foundational building block for semantic search, RAG pipelines, and recommendation systems.

Multimodal AI

AI systems that process and reason across multiple types of data simultaneously—text, images, audio, and video. Multimodal models enable richer enterprise applications such as document understanding that combines tables, charts, and prose.

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