The goal of contrastive representation learning is to learn such an embedding space in which similar sample pairs stay close to each other while dissimilar ones are far apart. Contrastive learning can be applied to both supervised and unsupervised settings. When working with unsupervised data, contrastive learning is one of the most powerful approaches in self-supervised learning.
Contrastive Training Objectives In early versions of loss functions for contrastive learning, only one positive and one negative sample are involved.
Accelerating Azure Databricks Runtime for Machine Learning
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Contrastive Representation Learning
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Nick D Burton
Making machines artificially intelligent is a time-consuming practice of collecting and manually labelling data for AIs to learn from. Today, researchers are enabling machines to learn new concepts continuously from far fewer data samples; in 2021, this will continue, as AI systems rely less on human labelling, and more on teaching themselves directly from interactions with users.
This will make a big difference to the “intelligence” of AI assistants. Today, it is second nature for us to complete transactional requests with AI assistants, either by issuing requests such as “Set the thermostat to 20°C”, or “Navigate to Wembley Stadium”. But, at the same time we are left yearning for more human-like conversational experiences, such as “Any ideas for this weekend?” or “Find me cameras under £400”. As we enter the next decade of AI assistants, advances in deep-learning architectures and associated learning techniques will take us towards this more na