Contrastive Learning
Contrastive learning is a self-supervised representation learning technique that trains a model to produce similar embeddings for semantically related (positive) pairs of examples and dissimilar embeddings for unrelated (negative) pairs.
7 resources across 1 library
Glossary Terms(7)
Federated Learning
Federated learning is a machine learning technique that trains a shared model across multiple decentralized devices or servers holding local data, without the…
One-Shot Learning
One-shot learning is a machine learning approach in which a model learns to correctly classify or recognize new categories from only a single labeled example p…
Semi-Supervised Learning
Semi-supervised learning is a machine learning approach that trains models using a combination of a small amount of labeled data and a much larger amount of un…
Contrastive Learning
Contrastive learning is a self-supervised representation learning technique that trains a model to produce similar embeddings for semantically related (positiv…
Multi-Task Learning
Multi-task learning is a machine learning technique in which a single model is trained simultaneously on multiple related tasks, sharing internal representatio…
Quantization (ML)
Quantization is a model compression technique that reduces the numerical precision used to represent a neural network's weights and activations (e.g., from 32-…
Mixed Precision Training
Mixed precision training is a technique for training neural networks that uses lower-precision numerical formats (such as 16-bit floating point) for most compu…