… representationlearning techniques have largely closed the gap between supervised and unsupervised learning … direction, we study contrastive selfsupervised learning on four diverse …
… that make representations from contrastivelearning more transferable … representations of contrastivelearning are much different than the cross-entropy model; in particular, contrastive …
… Our objective is to learnvisualrepresentation from multimodal data in a unified training framework. … We aim to learn high-quality visual features, which benefit from not only the additional …
C Yang, Z An, L Cai, Y Xu - Proceedings of the AAAI Conference on …, 2022 - ojs.aaai.org
… learning method called Mutual ContrastiveLearning (MCL) for general visualrepresentation learning… The core idea of MCL is to perform mutual interaction and transfer of contrastive …
A Diba, V Sharma, R Safdari, D Lotfi… - Proceedings of the …, 2021 - openaccess.thecvf.com
… -supervised visualrepresentationlearning … contrastivevisualrepresentationlearning for videos and images. The method, Video/Image for VisualContrastiveLearning of Representation …
… a viable alternative to learn dense representations without relying on video data [24… is to learn more effective representations through contrastive self-supervised learning without relying …
S Kim, G Lee, S Bae, SY Yun - arXiv preprint arXiv:2010.06300, 2020 - researchgate.net
… To validate the efficacy of MixCo, it is applied to recent popular representationlearning … extended the concept of contrastive loss for visualrepresentationlearning to semi-positive …
… In this work, we modify SimCLR [12], a state-of-the-art contrastiverepresentation method, by adding information compression using the Conditional Entropy Bottleneck (CEB) [27]. …
… a contrastivelearning framework where a Euclidean loss is used to learn object representations … In this paper, we aim at learningvisualrepresentations of both the cereal box (objects) …