multiple prototype的思路与multi-head attention类似,应用了之后可能还可以再高几个点。 参考文献: [1] Wang, Peng, et al. "Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification."Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021....
将两个分支连接建立了hybrid网络,该网络结构与BBN网络如出一辙。 第二,在Feature Learning阶段,作者采用了监督对比学习来学习Feature。在该步骤探索了两种用于特征学习的对比损失,一种是最近提出的有监督对比(SC)损失,在无监督对比损失基础上通过合并来自同一类的正样本。另一种是原型监督对比 (PSC) 学习策略,解决了...
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To tackle these challenges, this paper proposes a silent face liveness detection domain generalization model based on the fusion of convolutional neural network (CNN) and Swin Transformer features, namely, CLCSN. Then, a contrastive learning technique is suggested to highlight liveness-related style...
The feature representation of wind power sequences is crucial in the modeling of short-tern wind power forecast, but the existing feature representation methods mostly depend on the end-to-end model based on supervised learning, ignoring the superiority of self-supervised learning in the feature dis...
A hybrid loss function based on a sum of Dice and cross-entropy loss was selected for the training of the target task, and the training of the proxy task (Fig. 2B) was performed based on a root mean square error (RMSE) loss function [12]. Fig. 2 Self-learning procedure. A The ...
Deep learning has been widely used in remote sensing image classification and achieves many excellent results. These methods are all based on relatively balanced data sets. However, in real-world scenarios, many data sets belong to the long-tailed distribution, resulting in poor performance. In vie...
Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-EncoderYao Qiu, Jinchao Zhang, Jie ZhouFindings of ACL 2021[pdf] Contrastive Document Representation Learning with Graph Attention NetworksPeng Xu, Xinchi Chen, Xiaofei Ma, Zhiheng Huang, Bing XiangFindi...
Graph neural networks integrating contrastive learning have attracted growing attention in urban traffic flow forecasting. However, most existing graph con
Time-Contrastive Networks: Self-Supervised Learning from Video. Authors: Pierre Sermanet; Corey Lynch; Yevgen Chebotar; Jasmine Hsu; Eric Jang; Stefan Schaal; Sergey Levine. paper Contrastive Multiview Coding. Authors:Yonglong Tian, Dilip Krishnan, Phillip Isola. paper code Learning Video Represe...