MACHINE LEARNING MODELS TRAINED FOR MULTIPLE VISUAL DOMAINS USING CONTRASTIVE SELF-SUPERVISED TRAINING AND BRIDGE DOMAIN

MACHINE LEARNING MODELS TRAINED FOR MULTIPLE VISUAL DOMAINS USING CONTRASTIVE SELF-SUPERVISED TRAINING AND BRIDGE DOMAIN

An example a system includes a processor to receive a model that is a neural network and a number of training images. The processor can train the model using a bridge transform that converts the training images into a set of transformed images within a bridge domain. The model is trained using a con...

Ausführliche Beschreibung

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Bibliographische Detailangaben
Hauptverfasser: ARBELLE, Assaf, SCHWARTZ, Eliyahu, HARARY, Sivan, KARLINSKY, Leonid
Format: Patent
Sprache:eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Beschreibung
Zusammenfassung:An example a system includes a processor to receive a model that is a neural network and a number of training images. The processor can train the model using a bridge transform that converts the training images into a set of transformed images within a bridge domain. The model is trained using a contrastive loss to generate representations based on the transformed images.