… the InfoNCE loss for contrastive learning, which encourages representations to be closer to
… Unsupervised visual representation learning aims to learn image representations from pixels …

… visual representation learning by integrating the strengths of both cross-modal contrastive
learning … two SSL techniques in medical visual representation learning. One limitation of the …

… while enforcing diverse representations for other images. To … -learn, which is a library of
self-supervised methods for visual … -neighbor contrastive learning of visual representations. arXiv…

… Contrastive learning, which aims at minimizing the distance … applied in unsupervised feature
learning, where the design of … framework for contrastive learning of visual representations. …

… a contrastive learning approach, which leverages a multimodal encoder to obtain the multimodal
features (ie, visual, … Ultimately, the visual representations from the image encoder are …

… representation learning algorithms that use a contrastive loss have outperformed even
supervised learning … of reinforcement learning with representations learned via the contrastive loss…

… Instead, we aim at better leveraging inter-image invariance for contrastive learning to pursue
generic representations that improve both classification and dense prediction downstream …

… Contrastive self-supervised learning has shown impressive results in learning visual
representations from unlabeled images by enforcing invariance against different data …

… visual representation learning leads to revitalization of the classical information maximization
(InfoMax) principle [11]. A series of contrastive learning … images) and its representations (ie, …

… learning plannable visual representations along with complex dynamic models. In this work,
we … a new learning framework that jointly optimizes both the visual representation model and …