to

Supervised Online Hashing via Similarity Distribution Learning

Hashing based visual search has attracted extensive research Online hashing has attracted extensive research attention attention in recent years due to the rapid growth of when facing streaming data. Most online hashing visual data on the Internet [7, 33, 8, 26, 12, 13, 30, 32, 25, methods, learning binary codes based on pairwise similarities 35, 27]. In various scenarios, online hashing has become of training instances, fail to capture the semantic relationship, a hot topic due to the emergence of handling the streaming and suffer from a poor generalization in largescale data, which aims to resolve an online retrieval task by applications due to large variations. In this paper, we updating the hash functions from sequentially arriving data propose to model the similarity distributions between the input instances. On one hand, online hashing takes advantages data and the hashing codes, upon which a novel supervised of traditional offline hashing methods, i.e., low storage cost online hashing method, dubbed as Similarity Distribution and efficiency of pairwise distance computation in the Hamming based Online Hashing (SDOH), is proposed, to keep space. On the other hand, it also merits in training the intrinsic semantic relationship in the produced Hamming efficiency and scalability for large-scale applications, since space. Specifically, we first transform the discrete the hash functions are updated instantly and solely based on similarity matrix into a probability matrix via a Gaussianbased the current streaming data, which is superior to traditional normalization to address the extremely imbalanced hashing methods based on a hashing model entirely trained distribution issue. And then, we introduce a scaling Student from scratch.

Discriminative Similarity for Clustering and Semi-Supervised Learning

Similarity-based clustering and semi-supervised learning methods separate the data into clusters or classes according to the pairwise similarity between the data, and the pairwise similarity is crucial for their performance. In this paper, we propose a novel discriminative similarity learning framework which learns discriminative similarity for either data clustering or semi-supervised learning. The proposed framework learns classifier from each hypothetical labeling, and searches for the optimal labeling by minimizing the generalization error of the learned classifiers associated with the hypothetical labeling. Kernel classifier is employed in our framework. By generalization analysis via Rademacher complexity, the generalization error bound for the kernel classifier learned from hypothetical labeling is expressed as the sum of pairwise similarity between the data from different classes, parameterized by the weights of the kernel classifier. Such pairwise similarity serves as the discriminative similarity for the purpose of clustering and semi-supervised learning, and discriminative similarity with similar form can also be induced by the integrated squared error bound for kernel density classification. Based on the discriminative similarity induced by the kernel classifier, we propose new clustering and semi-supervised learning methods.

DNN-based Speaker Embedding Using Subjective Inter-speaker Similarity for Multi-speaker Modeling in Speech Synthesis

This paper proposes novel algorithms for speaker embedding using subjective inter-speaker similarity based on deep neural networks (DNNs). Although conventional DNN-based speaker embedding such as a $d$-vector can be applied to multi-speaker modeling in speech synthesis, it does not correlate with the subjective inter-speaker similarity and is not necessarily appropriate speaker representation for open speakers whose speech utterances are not included in the training data. We propose two training algorithms for DNN-based speaker embedding model using an inter-speaker similarity matrix obtained by large-scale subjective scoring. One is based on similarity vector embedding and trains the model to predict a vector of the similarity matrix as speaker representation. The other is based on similarity matrix embedding and trains the model to minimize the squared Frobenius norm between the similarity matrix and the Gram matrix of $d$-vectors, i.e., the inter-speaker similarity derived from the $d$-vectors. We crowdsourced the inter-speaker similarity scores of 153 Japanese female speakers, and the experimental results demonstrate that our algorithms learn speaker embedding that is highly correlated with the subjective similarity. We also apply the proposed speaker embedding to multi-speaker modeling in DNN-based speech synthesis and reveal that the proposed similarity vector embedding improves synthetic speech quality for open speakers whose speech utterances are unseen during the training.

The Local Elasticity of Neural Networks

This paper presents a phenomenon in neural networks that we refer to as \textit{local elasticity}. Roughly speaking, a classifier is said to be locally elastic if its prediction at a feature vector $\bx'$ is \textit{not} significantly perturbed, after the classifier is updated via stochastic gradient descent at a (labeled) feature vector $\bx$ that is \textit{dissimilar} to $\bx'$ in a certain sense. This phenomenon is shown to persist for neural networks with nonlinear activation functions through extensive simulations on real-life and synthetic datasets, whereas this is not observed in linear classifiers. In addition, we offer a geometric interpretation of local elasticity using the neural tangent kernel \citep{jacot2018neural}. Building on top of local elasticity, we obtain pairwise similarity measures between feature vectors, which can be used for clustering in conjunction with $K$-means. The effectiveness of the clustering algorithm on the MNIST and CIFAR-10 datasets in turn corroborates the hypothesis of local elasticity of neural networks on real-life data. Finally, we discuss some implications of local elasticity to shed light on several intriguing aspects of deep neural networks.

A Tour of Machine Learning Algorithms

Let's take a look at three different learning styles in machine learning algorithms: Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time. Example problems are clustering, dimensionality reduction and association rule learning. They are concerned with building much larger and more complex neural networks and, as commented on above, many methods are concerned with semi-supervised learning problems where large datasets contain very little labeled data. Like clustering methods, dimensionality reduction seek and exploit the inherent structure in the data, but in this case in an unsupervised manner or order to summarize or describe data using less information.