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 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 ...
《Few-shot learning with noisy labels》(CVPR 2022) GitHub: github.com/facebookresearch/noisy_few_shot《GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs》(NeurIPS 2022) GitHub: github.com/Emiyalzn/GraphDE [fig9]...
Build Data-To-Label Mappings Derive Task-To-Target Model Mappings Complementary Learning With Limited Information 1. DATA AUGMENTATION TO EVALUATE THE TRUE DATA DISTRIBUTION WITH MAXIMUM PROBABILITY 1.1 Hand-Crafted Rules 需要特定领域知识引导,根据信息维度,可分为data level和feature level。
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。
首先是根据一个已有的prototypes估计出partial assignment (其实就是类标签概率,在这可以用softmax也可以用OT并不限定方法),然后用这个类标签概率来构造affinity matrix (也就是图)。同志们发现了啥没,这种做法构造出来的图也是low rank的(和我们EASE异曲同工)。因为这个图是low rank的,此时使用label propagation对pa...
Equation (6) shows the total loss function, which was designed to boost the uncertainty learning branch to learn different uncertainty values for different facial images, where \(y_i\), \(y_j\) denote \(label_i\), \(label_j\), respectively, \(W_c\) is the c-th classifier, and ...
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 Direction Object Detection Segementation Generativ...
【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; ...