Age Progression/Regression by Conditional Adversarial Autoencoder

"If I provide you a face image of mine (without telling you the actual age when I took the picture) and a large amount of face images that I crawled (containing labeled faces of different ages but not necessarily paired), can you show me what I would look like when I am 80 or what I was like when I was 5?" The answer is probably a "No." Most existing face aging works attempt to learn the transformation between age groups and thus would require the paired samples as well as the labeled query image. In this paper, we look at the problem from a generative modeling perspective such that no paired samples is required. In addition, given an unlabeled image, the generative model can directly produce the image with desired age attribute. We propose a conditional adversarial autoencoder (CAAE) that learns a face manifold, traversing on which smooth age progression and regression can be realized simultaneously. In CAAE, the face is first mapped to a latent vector through a convolutional encoder, and then the vector is projected to the face manifold conditional on age through a deconvolutional generator. The latent vector preserves personalized face features (i.e., personality) and the age condition controls progression vs. regression. Two adversarial networks are imposed on the encoder and generator, respectively, forcing to generate more photo-realistic faces. Experimental results demonstrate the appealing performance and flexibility of the proposed framework by comparing with the state-of-the-art and ground truth.Comment: Accepted by The IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017

Vibrating Tail, Digging, Body/Face Interaction, and Lack of Barbering : Sex-Dependent Behavioral Signatures of Social Dysfunction in 3xTg-AD Mice as Compared to Mice with Normal Aging

Modeling of Alzheimer's disease (AD), classically focused on the subject-environment interaction, foresees current social neuroscience efforts as improving the predictive validity of new strategies. Here we studied social functioning among congeners in 13-14-month-old mice with normal aging in naturalistic and experimental conditions and depicted behavioral signatures of dysfunction in age-matched 3xTg-AD mice. The most sensitive variables were vibrating tail, digging, body/face and self-grooming, that can be easily used in housing routines and the assessment of strategies. Sex-specific signatures (vibrating tail, digging, and grooming) defined female 3xTg-AD mice ethogram. All animals sleep huddled while barbering was only found in females with normal aging