deep image clusteringdeep clustering subnetworkiterative refinement lossoverfitting trainingUnsupervised segmentation is an essential pre-processing technique in many computer vision tasks. However, current unsupervised segmentation techniques are sensitive to the parameters such as the segmentation numbers or ...
另一种半监督的方法为“self ensembling”,这些算法学习利用数据增强或模型设计中由噪声或随机性引起的随机扰动的鲁棒性。模型使用额外的损失项来连续正则化。 Methods A. SEMI-SUPERVISED LEARNING WITH DEEP EMBEDDED CLUSTERING 在分类任务中,卷积神经网络的目标是根据一个有标记的数据集 得到预测 。学习映射中,算法...
Deep clustering:Discriminative embeddings for segmentation and separation 简析 1.使用深度聚类的原因 在基于掩蔽的深度学习语音分离框架中,对于说话人相关的任务效果不错,但是对于说话人无关的任务,效果很差,会发生Permutation problem,即无法确定哪些维度上的信号属于目标声源,哪些维度属于干扰声源。 所以针对这个情况,He...
所以这里作者对图像进行了切割,切成了8和12份不等。 (二)Object seeds guided deep clustering 这里该篇文章参考了一篇显著性检测的文章。通过预测得到中间结果不断迭代得到更好的结果。 第一步,前面提到了是基于区域的,所以这里作者对图片进行了超像素(super-pixel)处理 参考文章:Efficient Graph-based Image Segmen...
DEEP CLUSTERING: DISCRIMINATIVE EMBEDDINGS FOR SEGMENTATION AND SEPARATION 非负矩阵分解是一种浅层的线性模型,很难挖掘语音数据复杂的非线性结构,此外使用非负矩阵分解的计算时间长,很难应用到实际应用中。 非负矩阵分解在语音分离中的应用是。首先非负矩阵分解可以将一个矩阵近似的分解为两个矩阵。于是在训练阶段,...
We hope that future work will lead to segmentation of arbitrary sounds, with extensions to microphone array methods as well as image segmentation and other domains. 展开 关键词: clustering speech separation embedding deep learning DOI: 10.1109/ICASSP.2016.7471631 被引量: 204 ...
Deep learning Medical image segmentation Multi-modality fusion Review 1. Introduction Segmentation using multi-modality has been widely studied with the development of medical image acquisition systems. Different strategies for image fusion, such as probability theory [1], [2], fuzzy concept [3], [...
DIC: Deep Image Clustering for Unsupervised Image Segmentation Unsupervised segmentation is an essential pre-processing technique in many computer vision tasks. However, current unsupervised segmentation techniques are... L Zhou,Y Wei - 《IEEE Access》 被引量: 0发表: 2020年 Place recognition with de...
Image(filename='model.png') The new DEC model can be compiled as follows: model.compile(optimizer=SGD(0.01, 0.9), loss='kld') model.get_layer(name='clustering').set_weights([kmeans.cluster_centers_]) 2.6 Training the New DEC Model ...
In most computer imaging scenarios, segmentation serves as the primary procedure for the further image understanding. In this paper, we propose a novel algorithm based on deep neural network and modified fuzzy clustering model. We enhance the clustering model with the incorporation of Markov random ...