Channel-Recurrent Autoencoding for Image Modeling

Despite recent successes in synthesizing faces and bedrooms, existing generative models struggle to capture more complex image types, potentially due to the oversimplification of their latent space constructions. To tackle this issue, building on Variational Autoencoders (VAEs), we integrate recurrent connections across channels to both inference and generation steps, allowing the high-level features to be captured in global-to-local, coarse-to-fine manners. Combined with adversarial loss, our channel-recurrent VAE-GAN (crVAE-GAN) outperforms VAE-GAN in generating a diverse spectrum of high resolution images while maintaining the same level of computational efficacy. Our model produces interpretable and expressive latent representations to benefit downstream tasks such as image completion. Moreover, we propose two novel regularizations, namely the KL objective weighting scheme over time steps and mutual information maximization between transformed latent variables and the outputs, to enhance the training.Comment: Code: https://github.com/WendyShang/crVAE. Supplementary Materials: http://www-personal.umich.edu/~shangw/wacv18_supplementary_material.pd

Pruning artificial neural networks: a way to find well-generalizing, high-entropy sharp minima

Recently, a race towards the simplification of deep networks has begun, showing that it is effectively possible to reduce the size of these models with minimal or no performance loss. However, there is a general lack in understanding why these pruning strategies are effective. In this work, we are going to compare and analyze pruned solutions with two different pruning approaches, one-shot and gradual, showing the higher effectiveness of the latter. In particular, we find that gradual pruning allows access to narrow, well-generalizing minima, which are typically ignored when using one-shot approaches. In this work we also propose PSP-entropy, a measure to understand how a given neuron correlates to some specific learned classes. Interestingly, we observe that the features extracted by iteratively-pruned models are less correlated to specific classes, potentially making these models a better fit in transfer learning approaches

Architectural perspectives in the cathedral of Palermo : image-based modeling for cultural heritage understanding and enhancement

Palermo off ers a repertoire of both artistic and architectural solid perspective of great beauty and in large quantity. This paper addresses the problem of the 3D survey of these works and their related study through the use of image-based modelling (IBM) techniques. We propose, as case studies, the use of IBM techniques inside the Cathedral of Palermo. Indeed, the church houses a huge and rich sculptural repertoire, dating back to 16th century, which constitutes a valid field of IBM techniques application. The aim of this study is to demonstrate the e ffectiveness and potentiality of these techniques for geometric analysis of sculptured works. Indeed, usually the survey of these artworks is very diffi cult due the geometric complexity, typical of sculptured elements. In this study, we analysed cylindrical and planar geometries as well as carrying out an application of perspective return.peer-reviewe

MVPNet: Multi-View Point Regression Networks for 3D Object Reconstruction from A Single Image

In this paper, we address the problem of reconstructing an object's surface from a single image using generative networks. First, we represent a 3D surface with an aggregation of dense point clouds from multiple views. Each point cloud is embedded in a regular 2D grid aligned on an image plane of a viewpoint, making the point cloud convolution-favored and ordered so as to fit into deep network architectures. The point clouds can be easily triangulated by exploiting connectivities of the 2D grids to form mesh-based surfaces. Second, we propose an encoder-decoder network that generates such kind of multiple view-dependent point clouds from a single image by regressing their 3D coordinates and visibilities. We also introduce a novel geometric loss that is able to interpret discrepancy over 3D surfaces as opposed to 2D projective planes, resorting to the surface discretization on the constructed meshes. We demonstrate that the multi-view point regression network outperforms state-of-the-art methods with a significant improvement on challenging datasets.Comment: 8 pages; accepted by AAAI 201

Preferred instantaneous vacuum for linear scalar fields in cosmological space-times

We discuss the problem of defining a preferred vacuum state at a given time for a quantized scalar field in Friedmann, Lema\^itre, Robertson Walker (FLRW) space-time. Among the infinitely many homogeneous, isotropic vacua available in the theory, we show that there exists at most one for which every Fourier mode makes vanishing contribution to the adiabatically renormalized energy-momentum tensor at any given instant. For massive fields such a state exists in the most commonly used backgrounds in cosmology, and provides a natural candidate for the ground state at that instant of time. The extension to the massless and the conformally coupled case are also discussed.Comment: 19 pages, 4 figures. Section VI was expanded to include a discussion on semi-classical gravity. Version to appear in PR