This paper proposed a "PixelGAN Autoencoder", for which the generative path is a convolutional autoregressive neural network on pixels, conditioned on a latent code, and the recognition path uses a generative adversarial network (GAN) to impose a prior distribution on the latent code. PixelGAN Autoencoder The key difference of PixelGAN Autoencoder from the previous "Adversarial Autoencoders" is that the normal deterministic decoder part of the network is replaced by a more powerful decoder -- "PixelCNN". Figure 2 shows that PixelGAN Autoencoder with Gaussian priors can decompose the global and local statistics of the images between the latent code and the autoregressive decode: Sub-figure 2(a) shows that the samples generated from PixelGAN have sharp edges with global statistics (it is possible to recognize the number from these samples). This paper keeps this advantage and modifies the architecture as follows: The normal decoder part of a conventional autoencoder is replaced by PixelCNN proposed in paper Conditional Image Generation with PixelCNN Decoders .
If so, we could just generate a bunch of synthetic images, capture real images of eyes, and without labeling any real images at all, learn this mapping--making the method cheap and easy to apply in practice. We first train the refiner network with only self-regularization loss, and introduce the adversarial loss after the refiner network starts producing blurry versions of the input synthetic images. The absolute difference between the estimated pupil center of synthetic and corresponding refined image is quite small: 1.1 /- 0.8px (eye width 55px). The absolute difference between the estimated pupil center of synthetic and corresponding refined image is quite small: 1.1 plus or minus 0.8 px (eye width fifty 5 px).
In the first place, to understand the context of adversarial machine learning, you should know about Machine Learning and Deep Learning in general. Adversarial machine learning studies various techniques where two or more sub-components (machine learning classifiers) have an opposite reward (or loss function). Most typical applications of adversarial machine learning are: GANs and adversarial examples. In GAN (generative adversarial network) you have two networks: generator and discriminator.
After manually paring these pictures so just the faces of the cats could be seen, Jolicoeur-Martineau fed the photos to a generative adversarial network (GAN). In this case, two algorithms are trained to recognize cat faces using the thousands of cat pictures from the database. These generated cat faces are then fed to the other algorithm, the discriminator, along with some pictures from the original training dataset. The discriminator attempts to determine which images are generated cat faces and which are real cat faces.
Yann LeCun, arguably the father of modern machine learning, has described Generative Adversarial Networks (GANs) as the most interesting idea in deep learning in the last 10 years (and there have been a lot of interesting ideas in Machine Learning over the past 10 years). You train the discriminator on real data to classify, say, an image as either a real photo or a non-photographic image. Given that the central problem of using Deep Learning models in business applications is lack of training data, this is a really big deal. This technology could, and probably should, form a pillar of next generation (big data and machine learning) risk management.
My first recollection of an effective Deep Learning system that used feedback loops where in "Ladder Networks". In an architecture developed by Stanford called "Feedback Networks", the researchers explored a different kind of network that feeds back into itself and develops the internal representation incrementally: In an even more recently published research (March 2017) from UC Berkeley have created astonishingly capable image to image translations using GANs and a novel kind of regularization. The major difficulty of training Deep Learning systems has been the lack of labeled data. So the next time you see some mind boggling Deep Learning results, seek to find the strange loops that are embedded in the method.
Astrophysicists are using artificial intelligence (AI) to create something like the technology in movies that magically sharpens fuzzy surveillance images: a network that could make a blurry galaxy image look like it was taken by a better telescope than it actually was. That could let astronomers squeeze out finer details from reams of observations. One is a generator that concocts images, the other a discriminator that tries to spot any flaws that would give away the manipulation, forcing the generator to get better. The team took thousands of real images of galaxies, and then artificially degraded them.
Machines might one day replace human laborers in a number of professions, but surely they won't ever replace human artists. The model used in this project involved a generator network, which produces the images, and a discriminator network, which "judges" whether it is art. The art that was generated by the system was then presented to human judges alongside human-produced art without revealing which was which. Of course, machines can't yet replace the meaning that's infused in works by human artists, but this project shows that artist skillsets certainly seem duplicatable by machines.
The artificial intelligence system proposed in the study is a Creative Adversarial Network (CAN), and expands upon a type of system known as a Generative Adversarial Network (GAN), the team explains in a paper published to arXiv. Researchers from Rutgers University, Facebook's AI Research lab, and College of Charleston fed the network 81,449 paintings from 1,119 artists across the 15th-20th centuries, encompassing a wide array of styles. The artificial intelligence system proposed in the study is a Creative Adversarial Network (CAN), and expands upon a type of system known as a Generative Adversarial Network (GAN), the team explains in a paper published to arXiv. Researchers from Rutgers University, Facebook's AI Research lab, and College of Charleston fed the network 81,449 paintings from 1,119 artists across the 15th-20th centuries, encompassing a wide array of styles.
In the art AI, one of these roles is played by a generator network, which creates images. The other is played by a discriminator network, which was trained on 81,500 paintings to tell the difference between images we would class as artworks and those we wouldn't – such as a photo or diagram, say. "You want to have something really creative and striking – but at the same time not go too far and make something that isn't aesthetically pleasing," says team member Ahmed Elgammal at Rutgers University. Once the AI had produced a series of images, members of the public were asked to judge them alongside paintings by people in an online survey, without knowing which were the AI's work.