Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

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Integrating single-cell transcriptomic data across different conditions, technologies, and species.

/pdf/convex-analysisnoer-san-nojin-zhan-nituite-5dl2rbmoyr.pdf

Convex Analysisの二,三の進展について

This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed

Face Description with Local Binary Patterns: Application to Face Recognition

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Heterogeneous face recognition (HFR) aims to match facial images acquired from different sensing modalities with mission-critical applications in forensics, security and commercial sectors. However, HFR is a much more challenging problem than traditional face recognition because of large intra-class variations of heterogeneous face images and limited training samples of cross-modality face image pairs. This paper proposes a novel approach namely Wasserstein CNN (convolutional neural networks, or WCNN for short) to learn invariant features between near-infrared and visual face images (i.e. NIR-VIS face recognition). The low-level layers of WCNN are trained with widely available face images in visual spectrum. The high-level layer is divided into three parts, i.e., NIR layer, VIS layer and NIR-VIS shared layer. The first two layers aims to learn modality-specific features and NIR-VIS shared layer is designed to learn modality-invariant feature subspace. Wasserstein distance is introduced into NIR-VIS shared layer to measure the dissimilarity between heterogeneous feature distributions. So W-CNN learning aims to achieve the minimization of Wasserstein distance between NIR distribution and VIS distribution for invariant deep feature representation of heterogeneous face images. To avoid the over-fitting problem on small-scale heterogeneous face data, a correlation prior is introduced on the fully-connected layers of WCNN network to reduce parameter space. This prior is implemented by a low-rank constraint in an end-to-end network. The joint formulation leads to an alternating minimization for deep feature representation at training stage and an efficient computation for heterogeneous data at testing stage. Extensive experiments on three challenging NIR-VIS face recognition databases demonstrate the significant superiority of Wasserstein CNN over state-of-the-art methods.

Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition

The interactive liveness detection for fact recognition often requires users to read some digits from 0 to 9. The movement and variation of lip texture during reading potentially provide discriminative information for human identification. This paper firstly addressed the issue of whether the lip texture during reading can serve as a soft-biometric for person identification. Different from the traditional lip recognition methods that are based on color statistics and lip shapes, we develop a deep architecture that incorporates both CNN and LSTM to jointly model the appearance and the spatial-temporal information of lip texture. We also build a new lip recognition database that contains 11,123 videos for the number 0∼9 in Chinese from 57 people. Experimental results show that the proposed method can achieve 96.01% on close-set protocols, suggesting the usage of lip texture as soft-biometrics for facilitating face recognition.

Person identification from lip texture analysis

Hashing techniques have been widely adopted for cross-modal retrieval due to its low storage cost and fast query speed. Most existing cross-modal hashing methods aim to map heterogeneous data into the common low-dimensional hamming space and then threshold to obtain binary codes by relaxing the discrete constraint. However, this independent relaxation step also brings quantization errors, resulting in poor retrieval performances. Other cross-modal hashing methods try to directly optimize the challenging objective function with discrete binary constraints. Inspired by [1], we propose a novel supervised cross-modal hashing method called Discrete Cross-Modal Hashing (DCMH) to learn the discrete binary codes without relaxing them. DCMH is formulated through reconstructing the semantic similarity matrix and learning binary codes as ideal features for classification. Furthermore, DCMH alternately updates binary codes of each modality, and iteratively learns the discrete hashing codes bit by bit efficiently, which is quite promising for large-scale datasets. Extensive empirical results on three real-world datasets show that DCMH outperforms the baseline approaches significantly.

Discrete Cross-Modal Hashing for Efficient Multimedia Retrieval

Dictionary learning has important applications in face recognition. However, large transformation variations of face images pose a grand challenge to conventional dictionary learning methods. A large portion of misleading dictionary atoms are usually learned to represent transformation factors, which will cause ambiguity in face recognition. To address this problem, this paper proposes a general framework for transform-invariant basis matrix learning. Specifically, we present a transform-invariant dictionary learning method which explicitly incorporates an appearance consistent error term to the original objective function in dictionary learning. The unified objective function is effectively optimized in an alternating iterative way. An ensemble of aligned images and a discriminative transform-invariant dictionary for sparse coding can be obtained by solving the formulated objective function. Experimental results on two public face databases demonstrate our algorithm's superiority compared with two state-of-the-art dictionary learning methods and the recently proposed transform-invariant PCA method.

Transform-invariant dictionary learning for face recognition

How to bridge heterogeneous gap between different modalities is one of the main challenges in cross-modal retrieval task. Most existing methods try to tackle this problem by projecting data from different modalities into a common space. In this paper, we introduce a novel X-Shaped Generative Adversarial Cross-Modal Network (X-GACMN) to learn a better common space between different modalities. Specifically, the proposed architecture combines the process of synthetic data generation and distribution adapting into a unified framework to make sure the heterogeneous modality distributions similar to each other in the learned common subspace. To promote the discriminative ability, a new loss function that combines intra-modality angular softmax loss and cross-modality pair-wise consistent loss is further imposed on the common space, hence the learned features can well preserve both inter-modality structure and intra-modality structure on a hypersphere manifold. Extensive experiments on three benchmark datasets show the effectiveness of the proposed approach.

Ran He

Papers

Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition

Person identification from lip texture analysis

Discrete Cross-Modal Hashing for Efficient Multimedia Retrieval

Transform-invariant dictionary learning for face recognition

X-GACMN: An X-Shaped Generative Adversarial Cross-Modal Network with Hypersphere Embedding