Feature combinations are essential for the success of many web applications, such as personalised recommendation and online advertising. State-of-the-art methods usually model explicit feature interactions to help neural networks reduce the number of parameters and achieve better performance. However, their explicit feature interactions are often restricted to the second-order due to computational complexity. In this work, we propose efficient ways to represent explicit high-order feature combinations as well as prune redundant features in the mean time. To begin with, we make novel use of the Count Sketch algorithm within a DNN classifier such that high-order feature combinations can be compactly represented. After that, to combat the problem of redundant features which degrade the prediction performance, we introduce an adaptive hashing algorithm, AutoHash, which can automatically select meaningful features to interact at high orders according to the specific dataset in question. This is an AutoML approach. Experiments on three well-known public datasets demonstrate that AutoHash is significantly superior to state-of-the-art methods. Meanwhile, due to its efficient scheme of automatically selecting useful high-order feature interactions, AutoHash has less model complexity and can be trained in an end-to-end manner with less training time than state-of-the-art methods.

Recommender systems powering online multi-stakeholder platforms often face the challenge of jointly optimizing multiple objectives, in an attempt to efficiently match suppliers and consumers. Examples of such objectives include user behavioral metrics (e.g. clicks, streams, dwell time, etc), supplier exposure objectives (e.g. diversity) and platform centric objectives (e.g. promotions). Jointly optimizing multiple metrics in online recommender systems remains a challenging task. Recent work has demonstrated the prowess of contextual bandits in powering recommendation systems to serve recommendation of interest to users. This paper aims at extending contextual bandits to multi-objective setting so as to power recommendations in a multi-stakeholder platforms.

Modelling feature interactions is key in Click-Through Rate (CTR) predictions. State-of-the-art models usually include explicit feature interactions to better model non-linearity in a deep network, but enumerating all feature combinations of high orders is not efficient and brings challenges to network optimization. In this work, we use AutoML to seek useful high-order feature interactions to train on without manual feature selection. For this purpose, an end-to-end model, AutoGroup, is proposed, which casts the selection of feature interactions as a structural optimization problem. In a nutshell, AutoGroup first automatically groups useful features into a number of feature sets. Then, it generates interactions of any order from these feature sets using a novel interaction function. The main contribution of AutoGroup is that it performs both dimensionality reduction and feature selection which are not seen in previous models. Offline experiments on three public large-scale benchmark datasets demonstrate the superior performance and efficiency of AutoGroup over state-of-the-art models. Furthermore, a ten-day online A/B test verifies that AutoGroup can be reliably deployed in production and outperform the commercial baseline by 10% on average in terms of CTR and CVR.

Recently, a popular line of research in face recognition is adopting margins in the well-established softmax loss function to maximize class separability. In this paper, we first introduce an Additive Angular Margin Loss (ArcFace), which not only has a clear geometric interpretation but also significantly enhances the discriminative power. Since ArcFace is susceptible to the massive label noise, we further propose sub-center ArcFace, in which each class contains K sub-centers and training samples only need to be close to any of the K positive sub-centers. Sub-center ArcFace encourages one dominant sub-class that contains the majority of clean faces and non-dominant sub-classes that include hard or noisy faces. Based on this self-propelled isolation, we boost the performance through automatically purifying raw web faces under massive real-world noise. Besides discriminative feature embedding, we also explore the inverse problem, mapping feature vectors to face images. Without training any additional generator or discriminator, the pre-trained ArcFace model can generate identity-preserved face images for both subjects inside and outside the training data only by using the network gradient and Batch Normalization (BN) priors. Extensive experiments demonstrate that ArcFace can enhance the discriminative feature embedding as well as strengthen the generative face synthesis.

Robust principal component analysis (RPCA) is a powerful method for learning low-rank feature representation of various visual data. However, for certain types as well as significant amount of error corruption, it fails to yield satisfactory results; a drawback that can be alleviated by exploiting domain-dependent prior knowledge or information. In this paper, we propose two models for the RPCA that take into account such side information, even in the presence of missing values. We apply this framework to the task of UV completion which is widely used in pose-invariant face recognition. Moreover, we construct a generative adversarial network (GAN) to extract side information as well as subspaces. These subspaces not only assist in the recovery but also speed up the process in case of large-scale data. We quantitatively and qualitatively evaluate the proposed approaches through both synthetic data and eight real-world datasets to verify their effectiveness.

We revisit the problem of robust principal component analysis with features acting as prior side information. To this aim, a novel, elegant, non-convex optimization approach is proposed to decompose a given observation matrix into a low-rank core and the corresponding sparse residual. Rigorous theoretical analysis of the proposed algorithm results in exact recovery guarantees with low computational complexity. Aptly designed synthetic experiments demonstrate that our method is the first to wholly harness the power of non-convexity over convexity in terms of both recoverability and speed. That is, the proposed non-convex approach is more accurate and faster compared to the best available algorithms for the problem under study. Two real-world applications, namely image classification and face denoising further exemplify the practical superiority of the proposed method.

Recently proposed robust 3D face alignment methods establish either dense or sparse correspondence between a 3D face model and a 2D facial image. The use of these methods presents new challenges as well as opportunities for facial texture analysis. In particular, by sampling the image using the fitted model, a facial UV can be created. Unfortunately, due to self-occlusion, such a UV map is always incomplete. In this paper, we propose a framework for training Deep Convolutional Neural Network (DCNN) to complete the facial UV map extracted from in-the-wild images. To this end, we first gather complete UV maps by fitting a 3D Morphable Model (3DMM) to various multiview image and video datasets, as well as leveraging on a new 3D dataset with over 3,000 identities. Second, we devise a meticulously designed architecture that combines local and global adversarial DCNNs to learn an identity-preserving facial UV completion model. We demonstrate that by attaching the completed UV to the fitted mesh and generating instances of arbitrary poses, we can increase pose variations for training deep face recognition/verification models, and minimise pose discrepancy during testing, which lead to better performance. Experiments on both controlled and in-the-wild UV datasets prove the effectiveness of our adversarial UV completion model. We achieve state-of-the-art verification accuracy, 94.05%, under the CFP frontal-profile protocol only by combining pose augmentation during training and pose discrepancy reduction during testing. We will release the first in-the-wild UV dataset (we refer as WildUV) that comprises of complete facial UV maps from 1,892 identities for research purposes.

Robust Principal Component Analysis (RPCA) aims at recovering a low-rank subspace from grossly corrupted high-dimensional (often visual) data and is a cornerstone in many machine learning and computer vision applications. Even though RPCA has been shown to be very successful in solving many rank minimisation problems, there are still cases where degenerate or suboptimal solutions are obtained. This is likely to be remedied by taking into account of domain-dependent prior knowledge. In this paper, we propose two models for the RPCA problem with the aid of side information on the low-rank structure of the data. The versatility of the proposed methods is demonstrated by applying them to four applications, namely background subtraction, facial image denoising, face and facial expression recognition. Experimental results on synthetic and five real world datasets indicate the robustness and effectiveness of the proposed methods on these application domains, largely outperforming six previous approaches.