Partial multi-label learningFew-shot learningMeta-learningWeakly-supervised learningNoisy labelsLabel correlationsPartial multi-label learning (PML) models the scenario where each training sample is annotated with a candidate label set, among which only a subset corresponds to the ground-truth labels. ...
partial-label (PL) samples for training. However, it is more common than not to have just few PL samples at hand when dealing with new tasks. Furthermore, existing few-shot learning algorithms assume precise labels of the support set; as such, irrelevant labels may seriously mislead the ...
Rogerio Feris, Raja Giryes, and Alex M Bronstein.Laso: Label-set operations networks for multi-label few-shot learning.In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6548–6557, 2019.
Because manual labeling is not considered, the label noise is almost inevitable such as incorrect or unstable label assignment for an unlabeled sample. In this study, a semi-supervised fine-tuning method is proposed to improve the fine-tuning performance by mining and labeling partial unlabeled ...
具体地,网络的输入把上一次的y (label)也作为输入,并且添加了external memory存储上一次的x输入,这使得下一次输入后进行反向传播时,可以让y (label)和x建立联系,使得之后的x能够通过外部记忆获取相关图像进行比对来实现更好的预测。这里的RNN就是meta-learner。
Learning a Deep ConvNet for Multi-label Classification with Partial Labels 论文笔记 Title: Learning a Deep ConvNet for Multi-label Classification with Partial Labels(2019) Link 文章目录 Abstract 1. Introduction 2. Related Work Learning with partial / missing labels. Curriculum Learning /... ...
2021-01-15论文笔记:Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces,程序员大本营,技术文章内容聚合第一站。
[NIPS 2019] (paper) Learning to Self-Train for Semi-Supervised Few-Shot Classification Label the query set for the first run, then retrain the model with the pesudo label for the second run. (Simple but effective) Results in Datasets ...
In this work, few-shot learning was used for both leaf and vein segmentation. In particular, each method divided a small number of large images into a large number of small image tiles and used the predictions of previous iterations to expand partial segmentations until stopping criteria was re...
(SBeA) used to overcome the problem caused by the limited datasets. SBeA uses a much smaller number of labelled frames for multi-animal three-dimensional pose estimation, achieves label-free identification recognition and successfully applies unsupervised dynamic learning to social behaviour ...