In multi-label classification, the main focus has been to develop ways of\nlearning the underlying dependencies between labels, and to take advantage of\nthis at classification time. Developing better feature-space representations\nhas been predominantly employed to reduce complexity, e.g., by ...
本文提出了一种新的端到端多标签图像分类方法,名为深度语义字典学习(Deep Semantic Dictionary Learning,DSDL),旨在解决上述问题。DSDL将多标签图像分类问题视为字典学习任务,并利用自编码器从类别级语义生成与视觉空间对齐的语义字典,使CNN提取的视觉特征可以通过语义字典表示其标签嵌入。与传统的多标签图像分类方法不同...
1 Deep Learning for Multi-label Classi?cation Jesse Read, Fernando Perez-Cruz Abstract—In multi-label classi?cation, the main focus has been to develop ways of learning the underlying dependencies between labels, and to take advantage of this at classi?cation time. Developing better feature-...
一、背景介绍 1、研究背景:Multi-label和二分类、多分类研究的内容本身就不太一样,并且Multi-label的数据稀疏问题比单一分类更严重,因此很难学习label之间的依赖关系。 2、研究问题:Extreme Multi-label Text Classification(XMTC)研究的是在一个非常大的标签空间中,为每一个文档找到最相关的若干标签(例如Wikipedia) ...
其中t\in\mathcal C_i的含义是当前像素点输入类别\mathcal C_i这一事件。又由于交叉熵CE_t=-\sum...
这个算法就是深度学习Deep Learning。借助于 Deep Learning 算法,人类终于找到了如何处理“抽象概念”这个亘古难题的方法。 机器学习(Machine Learning)是一门专门研究计算机怎样模拟或实现人类的学习行为,以获取新的知识或技能,重新组织已有的知识结构市值不断改善自身的性能的学科,简单地说,机器学习就是通过算法,使得机器...
In this section, we formally present our deep-learning based approach for multi-label classification. Generally speaking, our approach consists of three major components as demonstrated in Fig. 3: Domain-specific Pre-training, Label-attention and Multi-task learning. We will introduce the details of...
[1] Deep Learning for Extreme Multi-label Text Classification [0] 摘要 极端多标签文本分类(extreme multi-label text classification (XMTC))是指从一个非常大的标签集合为每个文档分类。巨大的特征空间、标签空间带来了数据稀疏性等挑战。
Task Alignment Learning(TAL),采用了TAL技术统一label assignment和task aligned loss,进一步提高模型精度;4. Efficient Task-aligned Head(ET-head), 改进了Head部分提出了一种ET-head,采用ESE(effictive SE)和shortcut来简化结构,采用Distribution Focal Loss来替换alignment,同时提升精度和速度。5. 改进Loss,结合VFL...
function [loss,gradients,Y] = modelLoss(X,T,parameters) Y = model(X,parameters); loss = crossentropy(Y,T,ClassificationMode="multilabel"); gradients = dlgradient(loss,parameters); end Model Predictions Function The modelPredictions function takes as input the model parameters, a word encodi...