We analyze key properties of the approach that make it work, finding that the contrastive loss outperforms a popular alternative based on cross-view prediction, and that the more views we learn from, the better the resulting representation captures underlying scene semantics.
Ranked #48 on Self-Supervised Action Recognition on UCF101
This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning.
Ranked #11 on Contrastive Learning on imagenet-1k
Neural net classifiers trained on data with annotated class labels can also capture apparent visual similarity among categories without being directed to do so.
Ranked #40 on Semi-Supervised Image Classification on ImageNet - 1% labeled data (Top 5 Accuracy metric)
The goal of self-supervised learning from images is to construct image representations that are semantically meaningful via pretext tasks that do not require semantic annotations for a large training set of images.
Ranked #7 on Contrastive Learning on imagenet-1k
Contrastive learning between multiple views of the data has recently achieved state of the art performance in the field of self-supervised representation learning.
Ranked #2 on Contrastive Learning on imagenet-1k
Contrastive unsupervised learning has recently shown encouraging progress, e. g., in Momentum Contrast (MoCo) and SimCLR.
Ranked #3 on Contrastive Learning on imagenet-1k