We formalize this problem as new learning paradigm called few-shot partial multi-label learning (FsPML), which aims to induce a noise-robust multi-label classifier with limited PML samples related to the target task. To address this problem, we propose a method named FsPML via prototype ...
Partial-label learning (PLL) generally focuses on inducing a noise-tolerant multi-class classifier by training on overly-annotated samples, each of which is annotated with a set of labels, but only one is the valid label. A basic promise of existing PLL solutions is that there are sufficient...
Partial-label learning (PLL) generally focuses on inducing a noise-tolerant multi-class classifier by training on overly-annotated samples, each of which is annotated with a set of labels, but only one is the valid label. A basic promise of existing PLL solutions is that there are sufficient...
Few-shot partial multi-label learning with synthetic features network Sun Yifan,Zhao Yunfeng,Yu GuoxianYan Zho... - 《Knowledge & Information Systems》 - 2024 - 被引量: 0 Self-Supervised Task Augmentation ...
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。
Amit Alfassy, Leonid Karlinsky, Amit Aides, Joseph Shtok, Sivan Harary, 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...
【19】Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learning; 【20】Multi-Pretext Attention Network for Few-shot Learning with Self-supervision; 【21】Local Propagation for Few-Shot Learning; 【22】Learning a Few-shot Embedding Model with Contrastive Learning; ...
Generative Based Few-shot Learning Papers Metric Based Few-shot Learning Traditional Semi-Supervised Supervised Special Unsorted External Memory Architecture Task Representation and Measure Multi Label Image Classification Add Additional Informations Self-training Results in Datasets mini-Imagenet More Dire...
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 /... ...
Then, we find the collision is caused by the label-irrelevant redundancies within the base-class feature and pixel space. Through qualitative and quantitative experiments, we identify this redundancy as the shortcut in the base-class training, which can be decoupled to alleviate the collision. ...