On gradient regularizers for MMD GANs

We propose a principled method for gradient-based regularization of the critic of GAN-like models trained by adversarially optimizing the kernel of a Maximum Mean Discrepancy (MMD). We show that controlling the gradient of the critic is vital to having a sensible loss function, and devise a method to enforce exact, analytical gradient constraints at no additional cost compared to existing approximate techniques based on additive regularizers. The new loss function is provably continuous, and experiments show that it stabilizes and accelerates training, giving image generation models that outperform state-of-the art methods on 160 × 160 CelebA and 64 × 64 unconditional ImageNet

The impact of Fair Trade on the living standards of farmers in Ghana. Social cooperatives and corporate cooperatives

Fair Trade is a complex movement, but its central purpose is to help disadvantaged small producers from developing countries through trade. The most recognized aspect of the movement is Fairtrade International, the biggest Fair Trade certification organization. The system was created to give advantages in international trade for farming cooperatives who decided to join the movement. Although in last 20 years of Fair Trade growth there have been many studies of the movement, there has only been one wide spectrum survey on the impact of Fair Trade on rural producers. Many others were concentrated at other aspects of the movement or were irrelevant. The research presented here is the first qualitative Fair Trade impact study conducted in Ghana. It also has a wide spectrum and is a part of broader ongoing research in two other regions of the world. Research was conducted to examine Fair Trade’s (in particular Fairtrade International’s) impact on farmers and communities in Ghana by comparing it to farmers and communities that do not benefit from the system. A qualitative study was conducted, based on 75 interviews, among them interviews with farmers of cocoa and oranges, members and employees of cooperatives, owners and employees of food companies and representatives of Fairtrade International. Observations of 5 villages and 1 small town, alongside the interviews conducted, resulted in the conclusion that there are no substantial differences in the standards of living between farmers which are members of cooperatives benefiting from Fair Trade and farmers from the comparison group. The research suggests that small cooperatives and big cooperatives function differently. Small cooperatives are operating in the interest of their members, while big cooperatives are focused on creating business potential. The study revealed that small cooperatives provide more benefits for farmers while the big ones transform into ventures similar to corporations

Zakres regulacji specjalistycznych funduszy inwestycyjnych w porządku prawnym Rzeczpospolitej Polskiej i Wielkiego Księstwa Luksemburga

Fundusze inwestycyjne uważane są za kluczowych uczestników rynku kapitałowego. Ich pozycja związana jest z uzyskaniem efektu skali działania oraz stosowaniem wysokiej jakości metod zarządzania kapitałem. Postępująca harmonizacja przepisów odnoszących się do ich funkcjonowania w ramach Unii Europejskiej umożliwiająca swobodny przepływ kapitału i transgraniczne oddziaływanie funduszy inwestycyjnych w poszczególnych państwach członkowskich stanowić może podstawę dla analiz porównawczych ukazujących wpływ, jaki rozwój przepisów odnoszących się do funduszy inwestycyjnych kierowanych do inwestorów nieprofesjonalnych oraz zmierzających do zapewnienia im właściwego poziomu bezpieczeństwa lokowania kapitału poprzez wyspecjalizowany nadzór państwowy i precyzyjną regulację działalności wywarł na poszczególne porządki krajowe

Demystifying MMD GANs

We investigate the training and performance of generative adversarial networks using the Maximum Mean Discrepancy (MMD) as critic, termed MMD GANs. As our main theoretical contribution, we clarify the situation with bias in GAN loss functions raised by recent work: we show that gradient estimators used in the optimization process for both MMD GANs and Wasserstein GANs are unbiased, but learning a discriminator based on samples leads to biased gradients for the generator parameters. We also discuss the issue of kernel choice for the MMD critic, and characterize the kernel corresponding to the energy distance used for the Cramer GAN critic. Being an integral probability metric, the MMD benefits from training strategies recently developed for Wasserstein GANs. In experiments, the MMD GAN is able to employ a smaller critic network than the Wasserstein GAN, resulting in a simpler and faster-training algorithm with matching performance. We also propose an improved measure of GAN convergence, the Kernel Inception Distance, and show how to use it to dynamically adapt learning rates during GAN training