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If your order is placed outside of normal office hours Monday to Friday , this may delay processing of your order. Please note: Digital products are non-refundable and cannot be exchanged once the order has been processed. Raine fights his way through Capital Prime, and transmits the activation code. The game ends with all Arks opening at the same time. The story of Rage 2 will take place thirty years after the events of the first game, building further on the premise of the original. Humanity has found ways to heal the world and restore some places into lush jungles, wetlands and in some places tundra.
Some type of Wild West society has emerged where trade is becoming more common. Still, people are brutal and the old world still remains. The Authority has returned and the delicate new biomes are under attack. You will play the role of Walker, one of the last rangers in the wasteland that got robbed by the Authority and is now seeking for vengeance. We will give you more information on Walker and his story in another article. The partner had no input in the creation or production of the content itself By Advertorial The world of Rage is a crazy post-apocalyptic wasteland, filled with weird but at the same time interesting people.
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The End of the World You might have heard about the asteroid called Apophis, which will pass earth by true story. The overall loss function is constructed in a way that penalizes the networks for not conforming to the above properties. Just make the networks use the exact same weights for some of the layers.
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There are many problems with training GANs. The most important of which is the training instability. Sometimes, the loss of the GAN can oscillate, as the generator and discriminator undo the learning of the other. Other times, your loss may explode right after the networks converge, and the images start looking horrible. ProGAN which stands for the progressive growing of generative adversarial networks is a technique that helps stabilize GAN training by incrementally increasing the resolution of the generated image. So ProGAN first trains a 4x4 generator and a 4x4 discriminator and adds layers that correspond to higher resolutions later in the training process.
The authors stuffed in a truckload of proofs, corollaries, another mathematical lingo into it. Just chuck out the old cost function, which approximates a statistical quantity called the Jensen-Shannon divergence, and slide in the new one, which approximates a statistical quantity called the 1-Wasserstein distance. The original GAN paper showed that when the discriminator is optimal, the generator is updated in such a way to minimize the Jensen-Shannon divergence.
You compute it like this:. A function that has has a constant value has a gradient equal to zero, and a zero gradient is bad because it means that the generator learns absolutely nothing. The alternate distance metric proposed by the WGAN authors is the 1-Wasserstein distance, sometimes called the earth mover distance. Imagine that one of the two distributions is a pile of earth, and the other is a pit.
Concretely no pun intended , the earth mover distance between two distributions can be written as:. Unfortunately, computing this is intractable.
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So instead, we compute something totally different:. Most of the work on WGAN is about providing a complex read rigorous justification for an admittedly simple idea.
Using transposed convolutions alone is like painting a picture while only looking at the canvas region within a small radius of your paintbrush. Even the greatest of artists, who manage to perfect the most exceptional and intricate details, need to take a step back and look at the big picture. They used a mystic technique of deep learning so powerful that it made the most technically advanced models quiver in fear as it overtook everything on the state of the art leaderboards with plenty left to spare.
Apart from getting all the eyeballs with the realistic imagery, BigGAN showed us some very detailed results of training GANs at large scales. The team behind BigGAN introduced a variety of techniques to combat the instability of training GANs on huge batch sizes across many machines. Notably, training seems to scale well by increasing parameters like batch size and width, but for some reason, collapses at the very end. If analyzing singular values to understand this instability sounds interesting to you, check out the paper, because you'll find a lot it there.
After the first version of the paper was released, the authors revisited BigGAN a few months later.
Turns out that was due to a poor architectural choice. Instead of just cramming more layers onto the model, the team experimented and found that using a ResNet bottleneck is the way to go. With all of the above tweaks, scaling, and careful experimentation, the top of the line BigGAN completely obliterates the previous state of the art Inception score of StyleGAN short for well, style generative adversarial network?
Having a world-class face generator that can fool most humans on planet earth is pointless if you want to generate images of cars. Instead, is a suite of techniques that can be used with any GAN to allow you to do all sorts of cool things like mix images, vary details at multiple levels, and perform a more advanced version of style transfer. To achieve this level of image style control, StyleGAN employs existing techniques like Adaptive instance normalization, a latent vector mapping network, and a constant learned input.
I have a detailed explanation of all the techniques, with a lot of cool results along the way. Wow, you made it to the end. You're all caught up on the latest breakthroughs in the highly academic domain of creating fake profile pics.
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But before you slump onto your couch and begin your endless scroll of your Twitter feed, take a moment to look at how far you've come:. But zoom in. Take a closer look. Do you see that show-shaped green patch of land?
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