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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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The key is: VAE usually use a small latent dimension, the information of input is so hard to pass through this bottleneck, meanwhile it tries to minimize the loss with the batch of input data, you should know the result -- VAE can only have a mean and blurry output. If you increase the bandwidth of the bottleneck, i.e. the size of latent vector, VAE can get ...


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In essence, Variational Autoencoders learn an "explicit" distribution of the data by trying to fit the data via a multi-dimensional Gaussian/Normal distribution. However, Generative Adversarial Networks learn an "implicit" distribution of data meaning that you cannot directly sample them. Also, due to the deterministic nature of neural networks GANs tend to ...


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in my experience GANs work really well for the scenario of semisupervised learning, where you don't necessarily have labels for all your class B data but you do have a balanced dataset. In my (limited) experience, you do have to have a balanced (in numbers) set of A and B objects, even though you are not sure of the labels. And yes, GANs can overfit to ...


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Can AI provide a more reliable analysis of the gross effects of carbon emissions on extinctions of species ice-cap melting, and other effects? Yes. The work of Judea Pearl and others over the last 20 years began out of a desire to address uncertainty within AI. Eventually, this led Pearl to become fascinated by the need to quantifiably determine when one ...


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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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Now I have a network trained to get an output value from an random set of attributes but, can I use this trained network to get the input attributes using only the desired output? It depends: If you are happy to find any inputs, even non-realistic ones, that get your desired output, then you can use your trained network, with a minor modification. Freeze ...


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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 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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I understand that you want to know about methods that we can use to evaluate GANs (Generative Adversarial Networks). How can GANs be evaluated? One Discriminator on Separate GANs We can train a Discriminator beforehand and then we can use this Discriminator on various Generators to see what does this Discriminator say about the images generated from each ...


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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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One of the big realizations that deep learning models brought in recent years was that we can train the feature extractors and classifiers simultaneously. In fact most people have stopped separating the 2 tasks and simply refer to all the process as training the model. However, if you dive in to every single model architecture, it will always be ...


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CRF is for HMM in sequences classification as Logistic Regression is for Naive Bayes for simple classification. For a in depth difference between them I strongly suggest you to read the classical Sutton article "An Introduction to Conditional Random Fields". For a more practical example explaining how to apply the math I suggest you to read this article.


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A generic way to start with under such circumstances is to try find an "oracle". Serial to parallel converters exist for quite some times, and some are open source (e.g. PIPS). The idea is to get serial code from step (1), use the "oracle" to produce parallel code, and that's it: Each conversion makes en entry in the dataset. Ensuring the quality of the ...


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