WebOct 13, 2024 · Our approach consists of three steps: (1) self-supervised pre-training on unlabeled natural images (using SimCLR); (2) further self-supervised pre-training using unlabeled medical data (using either SimCLR or MICLe); followed by (3) task-specific supervised fine-tuning using labeled medical data. WebApr 15, 2024 · Graph contrastive learning (GCL), by training GNNs to maximize the correspondence between the representations of the same graph in its different augmented forms, may yield robust and transferable ...
[2201.04309] Robust Contrastive Learning against Noisy Views
WebJan 1, 2024 · We investigate robust textual representation learning problems and introduce a disentangled contrastive learning approach. We introduce a unified model architecture to optimize the sub-tasks of feature alignment and uniformity, as … Web1. In this work, we introduce contrastive learning to effectively seize the internal consistency of objects, and then propose a contrastive learning-based robust object detection algorithm for smoke images. 2. Considering UAV view angle changes usually exist among photos shot by UAVs, we also propose a novel electrical 125 amp breaker panels
Self-supervised learning - Wikipedia
WebNov 3, 2024 · To this end, this work discards the prior practice [19, 31, 32, 56] of introducing AT to SSL frameworks and proposes a new two-stage framework termed Decoupled Adversarial Contrastive Learning (DeACL).At stage 1, we perform standard (i.e. non-robust) SSL to learn instance-wise representation as a target vector.At stage 2, the obtained … WebTo alleviate or even eliminate the influence of the false negatives caused by random sampling, we propose a noise-robust contrastive loss that could adaptively prevent the false negatives from dominating the network optimization. http://pengxi.me/wp-content/uploads/2024/03/2024CVPR-MvCLNwith-supp.pdf electric air tyre pump