… By combining these findings, we are able to considerably outperform previous methods for self-supervised and semi-supervisedlearning on ImageNet. A linear classifier trained on self-…
E Cole, X Yang, K Wilber… - … on Computer Vision …, 2022 - openaccess.thecvf.com
… As a first step in this direction, we studycontrastive selfsupervised learning on four diverse large-scale datasets. By looking through the lenses of data quantity, data domain, data quality…
D Lee, S Kim, I Kim, Y Cheon… - … on computer vision …, 2022 - openaccess.thecvf.com
… during training by the well-clustered features. On benchmarks of semi-supervisedlearning tasks, our contrastive … A simple framework for contrastivelearning of visualrepresentations. In …
J Li, C Xiong, SCH Hoi - … conference on computer vision, 2021 - openaccess.thecvf.com
… We propose CoMatch, a new semi-supervisedlearning … CoMatch jointly learns two representations of the training data… framework for contrastivelearning of visualrepresentations. …
… of our paper and CCLP is a promising direction for semi-supervisedlearning research. … Momentum contrast for unsupervised visualrepresentationlearning. arXiv preprint arXiv:…
Z Dai, B Cai, J Chen - … on Systems, Man, and Cybernetics (SMC …, 2022 - ieeexplore.ieee.org
… visualrepresentationlearning. However, there are a lot of supervised and semi-supervised … To fully utilize the label annotations, we propose Unified Momentum Contrast (UniMoCo), …
A Diba, V Sharma, R Safdari, D Lotfi… - … on computer vision, 2021 - openaccess.thecvf.com
… a joint self-supervised contrastivevisualrepresentationlearning for videos and images… semi-supervisedlearning. In Proceedings of the IEEE international conference on computer vision…
R Liu - arXiv preprint arXiv:2106.03259, 2021 - arxiv.org
… contrastivelearning method SimCLR [7] and in section 4.2 we introduce attempts to improve contrastive … In the supervised learning field and semi-supervisedlearning field, several …
… 44] or semi-supervisedlearning [6]. Recent self-supervised methods [4… is to study and improve contrastive self-supervised methods for learningvisualrepresentations. Self-supervised …