我们考虑一个由特征提取器组成的物体识别模型,Fθ:X→ZFθ:X→Z,其中ZZ是特征嵌入空间,以及分类器Gψ:Z→RCGψ:Z→RC,其中CC表示标签空间中的类别数。 3.5. Proxy-based Contrastive Learning Softmax 损失在学习类别代理方面效率高,实现了快速且安全的收敛,但不考虑样本与样本之间的关系。基于对比的损失利用了...
PCR: Proxy-based Contrastive Replay for Online Class-Incremental Continual Learning Huiwei Lin, Baoquan Zhang, Shanshan Feng*, Xutao Li, Yunming Ye Harbin Institute of Technology, Shenzhen {linhuiwei, zhangbaoquan}@stu.hit.edu.cn, {victor fengss, lixutao, y...
PCL: Proxy-based Contrastive Learning for Domain Generalization (CVPR'22) Official PyTorch implementation of PCL: Proxy-based Contrastive Learning in Domain Generalization. Xufeng Yao, Yang Bai, Xinyun Zhang, Yuechen Zhang, Qi Sun, Ran Chen, Ruiyu Li, Bei Yu Note that this project is built upo...
In particular, contrastive learning has shown great promises by (almost) matching the performance of fully-supervised CNN on vision tasks. Nonetheless, this method does not take advantage of available meta-data, such as participant's age, viewed as prior knowledge. Here, we propose to leverage...
all domains. We then design a feature enhancement module to filter out extraneous information from the textual descriptions, thereby optimizing and enriching this representation. Moreover, we present a contrastive learning auxiliary task to enhance a cross-domain sequence by weighing the importance of ...
Metric learning attempts to minimize a loss function in order to transform data into a more optimal representation for further applications. In this paper, we compare 4 different types of loss functions, e.g. 2 pair-based losses (Contrastive loss and Triplet Margin Ranking loss), and 2 proxy...
(Van de Meerendonk and Scheepers2004; Ayoub2014). Thus, the institutional shift was not immediately acknowledged by the general population, whose cultural models were (and largely still are) based on historical gender beliefs that define the essential, inherent and supposedly natural qualities of ...
Semi-Supervised Proxy Contrastive Generalization Network for Bearing Fault Diagnosisdoi:10.1007/978-3-031-73407-6_50Many cross-domain bearing fault diagnosis methods based on deep transfer learning have been emerged over the past few years. Nevertheless, most of these existing diagnostic methods make ...
Recent studies utilize patch-wise contrastive learning based on features from image-level self-supervised pretrained models. However, relying solely on similarity-based supervision from image-level pretrained models often leads to unreliable guidance due to insufficient patch-level semantic representations. ...
UCCH. proposes a novel momentum optimizer for learnable hashing in contrastive learning and designs a cross-modal ranking learning loss. Evaluation Protocols. We evaluated our method by comparing it with baseline approaches on two cross-modal retrieval tasks: image-to-text retrieval (I→T) and tex...