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There are several generative models that have been proposed before or roughly at the same time of the GAN (2014). For example, the deep Boltzman machine (2009), deep generative stochastic network (2014) or variational auto-encoder (2014).


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Probably the simplest way to search for an image with the highest probability of being a cat is to use a technique similar to Deep Dream: Load the network for training, but freeze all the network weights Create a random input image, and connect it to the network as a "variable" i.e. data that can be changed through training Set a loss function based on ...


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In deep networks there is actually a wide variety of solutions to the problem, but if you need to find one, any easy way to do this is just through normal optimization schemes $$\hat x = argmin_x \ L(y,x)$$ where $L(y,x)$ is your loss function. Since ANN's are generally differentiable you can optimize this iteratively with some form gradient descent scheme: $...


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Model means you can say a Prototype we make regarding to our task. As we first train our model on some observed or you can say bench-marked data ; called as TRaining phase of model. Then we apply that model to our problem (test data) you can say in order to evaluate how much well you have trained your model. Training data we use related to our task or use ...


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To understand this equation first you need to understand the context in which it is first introduced. We have two neural networks (i.e. $D$ and $G$) that are playing a minimax game. This means that they have competing goals. Let's look at each one separately: Generator Before we start, you should note that throughout the whole paper the notion of the data-...


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Generally, text generators work by modeling the joint distribution of the text by its Bayesian forward decomposition $ \begin{align*} p(w_1, w_2, ..., w_n) &= p(w_1) * p(w_2|w_1) * p(w_3|w_2, w_1) *\ ...\ * p(w_n|\{w_i\}_{i<n})\\ &= \prod_{i=1}^n p(w_i|\{w_k\}_{k<i})\\ \end{align*} $ From a modeling perspective, this looks right up ...


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I'll answer your questions one by one: In this equation are the $E_{z \sim p_z(z)}$ and $E_{x \sim p_{data}(x)}$ the means of the distributions of the mini batch samples? So let's take the first part $E_{x \sim p_{data}(x)}[log \,D(x)]$. This is read as the "expected value of $log \, D(x)$, where $x$ is sampled from $p_{data}(x)$". So, in simpler terms ...


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I just came across this piece of news yesterday: "This week, Microsoft Research threw down the gauntlet with the launch of a competition challenging researchers around the world to develop AI agents that can solve text-based games." This seems to be an AI competition announced by Microsoft with the aim to create AI that can solve text-based games. This ...


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I believe you may want to use a Sum Product Network for this task. SPNs are the state-of-the-art approach for face completion, and there are several more recent papers on this topic since the original above. Importantly, the SPN paper also covers other approaches that work well for this task. If lower-resolution results are acceptable for your task, PCA ...


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I don't think he said that at all. Going back to the talk you'll see he mentions mode collapse comes from the naivete of using alternating gradient-based optimization steps because then $min_{\phi}max_{\theta}L(G_\phi, D_\theta)$ starts to look a lot like $max_{\theta}min_{\phi}L(G_\phi, D_\theta)$. This is problematic because in the latter case the ...


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Short Answer Generative networks in generative network arrangements do not learn about input images directly. Their input during training is feedback from the discriminative network. The Theory in Summary The seminal paper, Generative Adversarial Networks, Goodfellow, Pouget-Abadie, Mirza, Xu, Warde-Farley, Ozair, Courville, and Bengio, June 2014, states, ...


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If you're building a straight "vanilla" generative adversarial network, it's best to understand the network as a statistical engine: You are training the generator on samples of a statistical distribution. (And you're training the discriminator to distinguish between "ground truth" images, and images from that generator.) Once you replace the input noise ...


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Your goal is to model a distribution when constructing a GAN, therefore you need a way to be able to sample that distribution. The noise's purpose is so you can do this. Generally, it's drawn from a distribution that is computationally easy to draw from (like a gaussian). You are modeling the generator $G(X)$ where $X \sim N(\mu, \sigma^2)$. this means $...


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With a Google Cloud V100 GPU the GAN would run a week to two with default parameters. Does this sound realistic time for this kind of dataset? It's definitely not feasible for me. Yes, V100s are quite beefy. You shouldn't even need a week. Obviously this is based on my experience with various problems, rather than a concrete calculation. Is 4000 ...


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The question is about a mismatch between the loss function in two papers on GANs. The first paper is Generative Adversarial Nets Ian J. Goodfellow et. al., 2014, and the excerpt image in the question is this. The adversarial modeling framework is most straightforward to apply when the models are both multilayer perceptrons. To learn the generator’s ...


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