18960623. Systems and Methods for Contrastive Learning of Visual Representations (Google LLC)
Systems and Methods for Contrastive Learning of Visual Representations
Organization Name
Inventor(s)
Simon Komblith of Toronto (CA)
Mohammad Norouzi of Toronto (CA)
Geoffrey Everest Hinton of Toronto (CA)
Kevin Jordan Swersky of Mississauga (CA)
Systems and Methods for Contrastive Learning of Visual Representations
This abstract first appeared for US patent application 18960623 titled 'Systems and Methods for Contrastive Learning of Visual Representations
Original Abstract Submitted
Systems, methods, and computer program products for performing semi-supervised contrastive learning of visual representations are provided. For example, the present disclosure provides systems and methods that leverage particular data augmentation schemes and a learnable nonlinear transformation between the representation and the contrastive loss to provide improved visual representations. Further, the present disclosure also provides improvements for semi-supervised contrastive learning. For example, computer-implemented method may include performing semi-supervised contrastive learning based on a set of one or more unlabeled training data, generating an image classification model based on a portion of a plurality of layers in a projection head neural network used in performing the contrastive learning, performing fine-tuning of the image classification model based on a set of one or more labeled training data, and after performing the fine-tuning, distilling the image classification model to a student model comprising a relatively smaller number of parameters than the image classification model.
