computer-vision-1
Contrastive Learning
3260 papers • 126 benchmarks • 313 datasets
Contrastive Learning is a deep learning technique for unsupervised representation learning. The goal is to learn a representation of data such that similar instances are close together in the representation space, while dissimilar instances are far apart. It has been shown to be effective in various computer vision and natural language processing tasks, including image retrieval, zero-shot learning, and cross-modal retrieval. In these tasks, the learned representations can be used as features for downstream tasks such as classification and clustering. (Image credit: Schroff et al. 2015)
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A Simple Framework for Contrastive Learning of Visual Representations
Geoffrey E. Hinton, Mohammad Norouzi, Ting Chen, Simon Kornblith•Wed Feb 12 2020
It is shown that composition of data augmentations plays a critical role in defining effective predictive tasks, and introducing a learnable nonlinear transformation between the representation and the contrastive loss substantially improves the quality of the learned representations, and contrastive learning benefits from larger batch sizes and more training steps compared to supervised learning.
