High-Resolution Deep Convolutional Generative Adversarial Networks

Generative Adversarial Networks (GANs) convergence in a high-resolution setting with a computational constrain of GPU memory capacity (from 12GB to 24 GB) has been beset with difficulty due to the known lack of convergence rate stability. In order to boost network convergence of DCGAN (Deep Convolutional Generative Adversarial Networks) and achieve good-looking high-resolution results we propose a new layered network structure, HDCGAN, that incorporates current state-of-the-art techniques for this effect. A novel dataset, Curt\'o & Zarza, containing human faces from different ethnical groups in a wide variety of illumination conditions and image resolutions is introduced. Curt\'o is enhanced with HDCGAN synthetic images, thus being the first GAN augmented face dataset. We conduct extensive experiments on CelebA (MS-SSIM 0.1978 and Distance of Fr\'echet 8.77) and Curt\'o.

Instance Hash Segmentation

We propose a novel approach to address the Simultaneous Detection and Segmentation problem. Using hierarchical structures we use an efficient and accurate procedure that exploits the hierarchy feature information using Locality Sensitive Hashing. We build on recent work that utilizes convolutional neural networks to detect bounding boxes in an image and then use the top similar hierarchical region that best fits each bounding box after hashing, we call this approach iSegmentation. We then refine our final segmentation results by automatic hierarchy pruning. iSegmentation introduces a train-free alternative to Hypercolumns. We conduct extensive experiments on PASCAL VOC 2012 segmentation dataset, showing that iSegmentation gives competitive state-of-the-art object segmentations.

McKernel: A Library for Approximate Kernel Expansions in Log-linear Time

Kernel Methods Next Generation (KMNG) introduces a framework to use kernel approximates in the mini-batch setting with SGD Optimizer as an alternative to Deep Learning. McKernel is a C++ library for KMNG ML Large-scale. It contains a CPU optimized implementation of the Fastfood algorithm that allows the computation of approximated kernel expansions in log-linear time. The algorithm requires to compute the product of Walsh Hadamard Transform (WHT) matrices. A cache friendly SIMD Fast Walsh Hadamard Transform (FWHT) that achieves compelling speed and outperforms current state-of-the-art methods has been developed. McKernel allows to obtain non-linear classification combining Fastfood and a linear classifier.