Generative adversarial networks (GANs) have seen widespread adoption in machine learning, especially in computer vision applications. These "generative" models are capable of producing artificial images in many instances indistinguishable from the real thing. The most common use of these networks is that of artificial face generation. These so-called "deepfakes" have been used in a number of research and commercial applications. With their proliferation, however, have come predictable problems of bias in their generation. All such models are trained on large datasets. Several pre-trained models for StyleGANs 2 and 3 are trained on the Flickr (FFHQ) dataset.

In the era of big data in scientific research, there is a necessity to leverage techniques which reduce human effort in labeling and categorizing large datasets by involving sophisticated machine tools. To combat this problem, we present a novel, general purpose model for 3D segmentation that leverages patch-wise adversariality and Long Short-Term Memory to encode sequential information. Using this model alongside citizen science projects which use 3D datasets (image cubes) on the Zooniverse platforms, we propose an iterative human-machine optimization framework where only a fraction of the 2D slices from these cubes are seen by the volunteers. We leverage the patch-wise discriminator in our model to provide an estimate of which slices within these image cubes have poorly generalized feature representations, and correspondingly poor machine performance. These images with corresponding machine proposals would be presented to volunteers on Zooniverse for correction, leading to a drastic reduction in the volunteer effort on citizen science projects. We trained our model on ~2300 liver tissue 3D electron micrographs. Lipid droplets were segmented within these images through human annotation via the `Etch A Cell - Fat Checker' citizen science project, hosted on the Zooniverse platform. In this work, we demonstrate this framework and the selection methodology which resulted in a measured reduction in volunteer effort by more than 60%. We envision this type of joint human-machine partnership will be of great use on future Zooniverse projects.

We developed a general deep learning framework, FluidGAN, datasets. In physics and engineering community, deep learning capable of learning and predicting time-dependent has introduced transformative solutions across diverse convective flow coupled with energy transport. However, is thoroughly data-driven with high speed and accuracy and most works are usually task-specific and still rely on satisfies the physics of fluid without any prior knowledge of understanding underlying physical rules. FluidGAN propose a FluidGAN model capable of inferring underlying also learns the coupling between velocity, pressure, and temperature physics and could directly predict stationary and timedependent fields. Our framework helps understand deterministic multi-physical phenomena using certain boundary multiphysics phenomena where the underlying physical conditions and initial conditions with both high accuracy model is complex or unknown.

It has been my impression, that in the immense space of Artificial Intelligence (AI) concepts and tools, Generative Adversarial Networks (GANs) stand aside as an untamed beast. Everybody realizes how powerful and cool they are, few know how to train them, and even fewer can actually find any use for them for a practical task. I might be wrong, so feel free to correct me. Meanwhile, I would like to take another look at this wonderful machinery and investigate its possible use for classification and embedding. GANs were introduced in reference [1]. They consist of two parts -- a discriminator and a generator. A discriminator is a function that takes in an object and converts it into a number. Of course, depending on the complexity of the object, it might be a formidable task to turn it into a number. For that reason, we might employ a pretty sophisticated function for a discriminator, like, for instance, a deep layered Convolutional Neural Network (CNN).

Anomaly detection is a classical problem where the aim is to detect anomalous data that do not belong to the normal data distribution. Current state-of-the-art methods for anomaly detection on complex high-dimensional data are based on the generative adversarial network (GAN). However, the traditional GAN loss is not directly aligned with the anomaly detection objective: it encourages the distribution of the generated samples to overlap with the real data and so the resulting discriminator has been found to be ineffective as an anomaly detector. In this paper, we propose simple modifications to the GAN loss such that the generated samples lie at the boundary of the real data distribution. With our modified GAN loss, our anomaly detection method, called Fence GAN (FGAN), directly uses the discriminator score as an anomaly threshold. Our experimental results using the MNIST, CIFAR10 and KDD99 datasets show that Fence GAN yields the best anomaly classification accuracy compared to state-of-the-art methods.

Recent work has shown that state-of-the-art models are highly vulnerable to adversarial perturbations of the input. We propose cowboy, an approach to detecting and defending against adversarial attacks by using both the discriminator and generator of a GAN trained on the same dataset. We show that the discriminator consistently scores the adversarial samples lower than the real samples across multiple attacks and datasets. We provide empirical evidence that adversarial samples lie outside of the data manifold learned by the GAN. Based on this, we propose a cleaning method which uses both the discriminator and generator of the GAN to project the samples back onto the data manifold. This cleaning procedure is independent of the classifier and type of attack and thus can be deployed in existing systems.