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 ...
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 ...
This is particularly vital in few-shot learning scenarios where labeled data is limited, enabling the models to generalize effectively from a small number of examples. The baseline method is constructed by the base learner and meta learner. The detail algorithm and pseudocode can be found in ...
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 ...
Finally, as the model uses a shared graph encoder for the joint optimization of self-supervised learning and the recommendation task, the time complexity of the multiview self-supervised learning task mainly comes from the self-supervised signals between views and the contrastive learning of item ...
Context-based and temporal-based self-supervised learning methods are mainly used in text and video, while the scheme of SEI is mainly based on signal processing. Therefore, contrastive-based self-supervised learning is a better choice. State-of-the-art contrastive methods [22,24,29,30] are ...
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...
In addition, the performance of MVAE and MAAE for feature learning is improved with increased window size, which may be one of the parameters related to the results, whereas changing the parameter almost has few impacts on the learning of contrastive features. More valuable information could be...