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.

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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

Multi-view clustering, which aims to cluster datasets with multiple sources of information, has a wide range of applications in the communities of data mining and pattern recognition. Generally, it makes use of the complementary information embedded in multiple views to improve clustering performance. Recent methods usually find a low-dimensional embedding of multi-view data, but often ignore some useful prior information that can be utilized to better discover the latent group structure of multi-view data. To alleviate this problem, a novel pairwise sparse subspace representation model for multi-view clustering is proposed in this paper. The objective function of our model mainly includes two parts. The first part aims to harness prior information to achieve a sparse representation of each high-dimensional data point with respect to other data points in the same view. The second part aims to maximize the correlation between the representations of different views. An alternating minimization method is provided as an efficient solution for the proposed multi-view clustering algorithm. A detailed theoretical analysis is also conducted to guarantee the convergence of the proposed method. Moreover, we show that the must-link and cannot-link constraints can be naturally integrated into the proposed model to obtain a link constrained multi-view clustering model. Extensive experiments on five real world datasets demonstrate that the proposed model performs better than several state-of-the-art multi-view clustering methods. (C) 2015 Elsevier B.V. All rights reserved.

Multi-view Clustering via Pairwise Sparse Subspace Representation. Neurocomputing

Face recognition is a significant and pervasively applied computer vision task. With the specific application scenarios being explored gradually, general face recognition methods dealing with visible light images are unqualified. Cross-domain face recognition refers to a series of methods in response to face recognition problems whose inputs may come from multiple modalities, such as visible light images, sketch, near infrared images, 3D data, low-resolution images, thermal infrared images, or cross different ages, expressions, and ethnicities. Compared with general face recognition, cross-domain face recognition has not been widely explored and only few literatures systematically discuss this topic. Face recognition aiming at matching face images from photographs and other image modalities, which is usually called heterogeneous face recognition, has larger cross-domain gap and is a harder problem in this topic. This paper mainly investigates heterogeneous face databases, provides an up-to-date review of research efforts, and addresses common problems and related issues in cross-domain face recognition techniques.

Recent Advances on Cross-Domain Face Recognition

Out-of-distribution (OOD) generalization is an important issue for Graph Neural Networks (GNNs). Recent works employ different graph editions to generate augmented environments and learn an invariant GNN for generalization. However, the label shift usually occurs in augmentation since graph structural edition inevitably alters the graph label. This brings inconsistent predictive relationships among augmented environments, which is harmful to generalization. To address this issue, we propose \textbf{LiSA}, which generates label-invariant augmentations to facilitate graph OOD generalization. Instead of resorting to graph editions, LiSA exploits \textbf{L}abel-\textbf{i}nvariant \textbf{S}ubgraphs of the training graphs to construct \textbf{A}ugmented environments. Specifically, LiSA first designs the variational subgraph generators to extract locally predictive patterns and construct multiple label-invariant subgraphs efficiently. Then, the subgraphs produced by different generators are collected to build different augmented environments. To promote diversity among augmented environments, LiSA further introduces a tractable energy-based regularization to enlarge pair-wise distances between the distributions of environments. In this manner, LiSA generates diverse augmented environments with a consistent predictive relationship and facilitates learning an invariant GNN. Extensive experiments on node-level and graph-level OOD benchmarks show that LiSA achieves impressive generalization performance with different GNN backbones. Code is available on \url{https://github.com/Samyu0304/LiSA}.

Mind the Label Shift of Augmentation-based Graph OOD Generalization

Visible (VIS) to near infrared (NIR) face matching is a challenging problem due to the significant domain discrepancy between the domains and a lack of sufficient data for training cross-modal matching algorithms. Existing approaches attempt to tackle this problem by either synthesizing visible faces from NIR faces, extracting domain-invariant features from these modalities, or projecting heterogeneous data onto a common latent space for cross-modal matching. In this paper, we take a different approach in which we make use of the Disentangled Variational Representation (DVR) for cross-modal matching. First, we model a face representation with an intrinsic identity information and its within-person variations. By exploring the disentangled latent variable space, a variational lower bound is employed to optimize the approximate posterior for NIR and VIS representations. Second, aiming at obtaining more compact and discriminative disentangled latent space, we impose a minimization of the identity information for the same subject and a relaxed correlation alignment constraint between the NIR and VIS modality variations. An alternative optimization scheme is proposed for the disentangled variational representation part and the heterogeneous face recognition network part. The mutual promotion between these two parts effectively reduces the NIR and VIS domain discrepancy and alleviates over-fitting. Extensive experiments on three challenging NIR-VIS heterogeneous face recognition databases demonstrate that the proposed method achieves significant improvements over the state-of-the-art methods.

Disentangled Variational Representation for Heterogeneous Face Recognition

Recent studies have shown that the performance of forgery detection can be improved with diverse and challenging Deepfakes datasets. However, due to the lack of Deepfakes datasets with large variance in appearance, which can be hardly produced by recent identity swapping methods, the detection algorithm may fail in this situation. In this work, we provide a new identity swapping algorithm with large differences in appearance for face forgery detection. The appearance gaps mainly arise from the large discrepancies in illuminations and skin colors that widely exist in real-world scenarios. However, due to the difficulties of modeling the complex appearance mapping, it is challenging to transfer fine-grained appearances adaptively while preserving identity traits. This paper formulates appearance mapping as an optimal transport problem and proposes an Appearance Optimal Transport model (AOT) to formulate it in both latent and pixel space. Specifically, a relighting generator is designed to simulate the optimal transport plan. It is solved via minimizing the Wasserstein distance of the learned features in the latent space, enabling better performance and less computation than conventional optimization. To further refine the solution of the optimal transport plan, we develop a segmentation game to minimize the Wasserstein distance in the pixel space. A discriminator is introduced to distinguish the fake parts from a mix of real and fake image patches. Extensive experiments reveal that the superiority of our method when compared with state-of-the-art methods and the ability of our generated data to improve the performance of face forgery detection.

Ran He

Papers

Multi-view Clustering via Pairwise Sparse Subspace Representation. Neurocomputing

Recent Advances on Cross-Domain Face Recognition

Mind the Label Shift of Augmentation-based Graph OOD Generalization

Disentangled Variational Representation for Heterogeneous Face Recognition

AOT: Appearance Optimal Transport Based Identity Swapping for Forgery Detection