In our implementation, The E2E-VarNet was trained over 100 epochs with a minibatch size of 4, using an RMSProp optimizer with a learning rate set at 0.001. The pseudocode for the contrastive learning process of a sampled batch is shown in Algorithm 1. Algorithm 1 CL-MRI Input: batch ...
Besides using contrastive learning approaches, Masked Image Modeling (MIM) is an emerging pretext task first proposed in BEiT19. MIM applies block-wise random masking on discrete19 or continuous20,21 patch tokens and then considers recovering the masked patch tokens or pixels as an auxiliary task...
Semi-supervised semantic segmenta- tion with pixel-level contrastive learning from a class-wise memory bank. In ICCV, 2021. 3 [2] Eric Arazo, Diego Ortego, Paul Albert, Noel E O'Connor, and Kevin McGuinness. Pseudo-labeling and confirmation bias in deep ...
A supervised contrastive learning method for HSI classification is designed. In SCL, the labeled data are paired to pre-train a CNN-based feature encoder by the proposed supervised contrastive loss. To increase the diversity of the data pairs in a mini-batch and thus benefit the training procedu...
A supervised contrastive learning method for HSI classification is designed. In SCL, the labeled data are paired to pre-train a CNN-based feature encoder by the proposed supervised contrastive loss. To increase the diversity of the data pairs in a mini-batch and thus benefit the training procedu...
A simple framework for contrastive learning of visual representations. In Proceedings of the International Conference on Machine Learning, Virtual, 13–18 July 2020; pp. 1597–1607. [Google Scholar] Toering, M.; Gatopoulos, I.; Stol, M.; Hu, V.T. Self-supervised video representation ...
Improved baselines with momentum contrastive learning. arXiv 2020, arXiv:2003.04297. [Google Scholar] Hu, H.; Wei, F.; Hu, H.; Ye, Q.; Cui, J.; Wang, L. Semi-supervised semantic segmentation via adaptive equalization learning. Adv. Neural Inf. Process. Syst. 2021, 34, 22106–22118...
Self-supervised learning has various forms based on the domain. Self-Writer aligns with contrastive self-supervised learning strategies [55]. In order to learn from self-supervised learning, the system must define a self-supervision task. In general, self-supervised learning receives supervision signa...
[31] shifted their attention to imposing transformations on multimodal data and combining them with existing noise-contrastive learning methods and achieved astonishing results on several downstream tasks. These studies show that a carefully designed self-supervised framework can help the network effectively...