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Generative adversarial networks (GANs) have recently achieved impressive results in facial age synthesis. However, these methods usually select an autoencoder-style generator. And the bottleneck layer in the encoder-decoder generally gives rise to blurry and low-quality generation. To address this limitation, we propose a novel attention-aware conditional generative adversarial network (ACGAN). First, we utilize two different attention mechanisms to improve generation quality. On one hand, we integrate channel attention modules into the generator to enhance the discriminative representation power. On the other hand, we introduce a position attention mask to well-process images captured with various backgrounds and illuminations. Second, we deploy a local discriminator to enhance the central face region with informative details. Third, we adopt three types of losses to achieve accurate age generation and preserve personalized features: 1) The adversarial loss aims to synthesize photo-realistic faces with expected aging effects; 2) The identity loss intends to keep identity information unchanged; 3) The attention loss tries to improve the accuracy of attention mask regression. To assess the effectiveness of the proposed method, we conduct extensive experiments on several public aging databases. Experimental results on MORPH, CACD, and FG-NET show the effectiveness of the proposed framework.

This work aims to freely translate an input face to an aging face with robust identity preservation, satisfying aging effect and authentic visual appearance. Witnessing the success of GAN in image synthesis, researchers employ GAN to address the problem of face aging synthesis. However, most GAN-based methods hold that the aging changing of all facial regions is equal, which ignores the fact that different facial regions have distinct aging speeds and aging patterns. To this end, we propose a novel Conditioned-Attention Normalization GAN (CAN-GAN) for age synthesis by leveraging the aging difference between two age groups to capture facial aging regions with different attention factors. In particular, a new Conditioned-Attention Normalization (CAN) layer is designed to enhance the aging-relevant information of face, while smoothing the aging-irrelevant information of face by attention map. Since different facial attributes contribute to the discrimination of age groups with divers degrees, we further present a Contribution-Aware Age Classifier (CAAC) that finely measures the importance of face vector’s elements in terms of the age classification. Qualitative and quantitative experiments on several commonly-used datasets show the advance of CAN-GAN compared with the other competitive methods.

Overlapping community detection, dynamic community identification and community evolution analysis are the three important problems for social network analysis. It is a challenging task to simultaneously address all these three problems with one single method, thus most traditional studies focus on only one or two of them. This paper proposes a novel Dynamic Overlapping Community Evolution Tracking (DOCET) method to solve the three problems simultaneously with one single model, i.e. topology potential field. Specifically, the proposed DOCET method first detects the initial overlapping community structure based on node location analysis in the peak-valley structure of the topology potential field; then it incrementally updates the dynamic community structure based on influence scope analysis in the topology potential field; finally it tracks community evolution events based on the variation of core nodes in the topology potential field. Experiment results on both synthetic and real-world networks show that our proposed method achieves remarkable performance over the existing state-of-the-art methods. It can both accurately partition dynamic overlapping social networks and efficiently track all kinds of community evolution events.