Title: 《PLMCL: Partial-Label Momentum Curriculum Learning for Multi-Label Image Classification》 ECCV 2022w Highlight 提出了一个新的partial-label的multi-label setting,只有一部分数据有部分标签,另一部分数据没有标签。 引入了momentum更新pseudo label的方法,类似于EMA,还把课程学习那一套的概念引入了进来。
clf.partial_fit(train_X, train_y, classes=[[0,1]] *len(label_vocab))break# testtest_X, test_y = load_test_data()# evaluate for each test filey_pred = clf.predict_proba(test_X)# [n_tags, n_test_unit]y_pred_prcessed = []foriinrange(len(test_X)): test_tmp = []forjin...
几篇论文实现代码:《Long-Tailed Partial Label Learning via Dynamic Rebalancing》(ICLR 2023) GitHub: github.com/MediaBrain-SJTU/RECORDS-LTPLL [fig3] 《3D Concept Grounding on Neural Fields》(NeurIP...
Partial Multi-Label Learning常用数据集.zip泪不**肯走 上传20.73MB 文件格式 zip Partial Multi-Label Learning常用数据集 点赞(0) 踩踩(0) 反馈 所需:1 积分 电信网络下载 (Unity插件)Advance Sniper Starter Kit 2024-12-18 10:06:10 积分:1 ...
为了解决这样的问题，我们提出了偏多标记学习框架(Partial Multi-label Learning, PML)。首先来看一个现实...
Partial multi-label learning (PML) aims to learn from training examples each associated with a set of candidate labels, among which only a subset are valid for the training example. The common strategy to induce predictive model is trying to disambiguate the candidate label set, such as identify...
代码可以在https://github.com/Alibaba-MIIL/PartialLabelingCSL找到。 1. Introduction 1. 引言 Recently, a remarkable progress has been made in multi-label classification [4, 7, 16, 29]. Dedicated loss functions were proposed in [2, 27], as well as transformers based approaches [5, 16, 19...
Partial multi-label learning (PML) models the scenario where each training instance is annotated with a set of candidate labels, and only some of the labels are relevant. The PML problem is practical in real-world scenarios, as it is difficult and even impossible to obtain precisely labeled sa...
input3 --> label3 — 32% data input4 --> label1,label2 — 1% data input5 --> label1,label3 — 1% data input6 --> label2,label3 — 1% data input7 --> label1,label2,label3 — 1% data 解决思路: 《Learning a Deep ConvNet for Multi-label Classification with Partial Labels》 ...
代表多层感知机(a multi-layer perceptron(MLP))。 Hidden state update function F: 上式中GRU代表gated recurrent unit(门循环单元),隐状态更新就是一句输入信息和先前的隐状态; 3.3 对于未知标签的预测 作者结合课程学习(a curriculum learning strategy)进行丢失label的预测,用于优化以下目标函数: ...