Questions tagged [generative-adversarial-networks]

For questions related to generative adversarial networks (GANs), introduced in the paper Generative Adversarial Nets (2014) by J. Goodfellow et al. A GAN is composed of a discriminative model (D) and a generative model (G). The discriminator D needs to distinguish between data generated by the generator G and data in the training set, while the generator G needs to generate data such that the discriminator D is not able to accomplish its task.

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20
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3answers
25k views

How can we process the data from both the true distribution and the generator?

I'm struggling to understand the GAN loss function as provided in Understanding Generative Adversarial Networks (a blog post written by Daniel Seita). In the standard cross-entropy loss, we have an ...
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2answers
2k views

How are generative adversarial networks trained?

I am reading about generative adversarial networks (GANs) and I have some doubts regarding it. So far, I understand that in a GAN there are two different types of neural networks: one is generative ($...
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1answer
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Understanding notation of Goodfellow's GAN objective function

What is the meaning of $V(D,G)$? How do we get these expectation parts? I was trying to understand it following this article: Understanding Generative Adversarial Networks (D.Seita), but, after many ...
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3answers
439 views

Can some one help me understand this paragraph from Nvidia's progressive GAN paper?

Furthermore, we observe that mode collapses traditionally plaguing GANs tend to happen very quickly, over the course of a dozen mini-batches. Commonly they start when the discriminator overshoots, ...
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2answers
7k views

Why is it called Latent Vector?

I just learned about GAN and I'm a little bit confused about the naming of Latent Vector. First, In my understanding, a definition of a latent variable is a random variable that can't be measured ...
10
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1answer
267 views

Can I start with perfect discriminator in GAN?

In many implementations/tutorials of GANs that I've seen so far (e.g. this), the generator and discriminator start with no prior knowledge. They continuously improve their performance with training. ...
9
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3answers
2k views

Do GANs come under supervised learning or unsupervised learning?

Do GANs come under supervised learning or unsupervised learning? My guess is that they come under supervised learning, as we have labeled dataset of images, but I am not sure as there might be other ...
8
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0answers
92 views

Why is my GAN more unstable with bigger networks?

I am working with generative adversarial networks (GANs) and one of my aims at the moment is to reproduce samples in two dimensions that are distributed according to a circle (see animation). When ...
7
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1answer
224 views

How does the generator in GAN's work?

After reading a lot of articles (for instance, this one - https://developers.google.com/machine-learning/gan/generator), I've been wondering: how does the generator in GAN's work? What is the input ...
7
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1answer
1k views

What are the fundamental differences between VAE and GAN for image generation?

Starting from my own understanding, and scoped to the purpose of image generation, I'm well aware of the major architectural differences: A GAN's generator samples from a relatively low dimensional ...
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2answers
162 views

Why don't we use auto-encoders instead of GANs?

I have watched Stanford's lectures about artificial intelligence, I currently have one question: why don't we use autoencoders instead of GANs? Basically, what GAN does is it receives a random vector ...
5
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Why are Variational autoencoder's output is blurred while GANs output is crisp and has sharp edges?

I observed in several papers that the Variational autoencoder's output is blurred while GANs output is crisp and has sharp edges. Can someone please give some intuition why that is the case? I did ...
5
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1answer
69 views

Why is the mean used to compute the expectation in the GAN loss?

From Goodfellow et al. (2014), we have the adversarial loss: $$ \min_G \, \max_D V (D, G) = \mathbb{E}_{x∼p_{data}(x)} \, [\log \, D(x)] \\ \quad\quad\quad\quad\quad\quad\quad + \, \mathbb{E}_{z∼p_z(...
5
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1answer
144 views

Isn't deep fake detection bound to fail?

Deep fakes are a growing concern: the ability to credibly alter a video may have great (negative) impacts on our society. It is so much of a concern, that the biggest tech companies launched a ...
5
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1answer
120 views

Can GANs be used to generate something other than images?

AFAIK, GANs are used for generating/synthesizing near-perfect human faces (deepfakes), gallery arts, etc., but can GANs be used to generate something other than images?
5
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1answer
159 views

Why does a Lipschitz continuous discriminator in GANs assure statistical boundedness?

I have been reading the paper which introduced spectral normalization in GANs. At some point the paper mentions the following: The machine learning community has been pointing out recently that ...
5
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1answer
390 views

Why is Jensen-Shannon divergence preferred over Kullback-Leibler divergence in measuring the performance of a generative network?

I have read articles on how Jensen-Shannon divergence is preferred over Kullback-Leibler in measuring how good a distribution mapping is learned in a generative network because of the fact that JS-...
5
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3answers
144 views

What is an "adversarial attack"

I'm reading this really interesting article CycleGAN, a Master of Steganography and I understand everything up until this paragraph: we may view the CycleGAN training procedure as continually ...
5
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1answer
3k views

Are deep learning models suitable for training with sparse data?

I am training a generative adversarial network (GAN) to generate images given edge histogram descriptor (EHD) features of the image. The EHD features are themselves sparse (meaning they contain a lot ...
5
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1answer
190 views

Context-based gap-fill face posture-mapper GAN

These images are handmade, not auto-generated like they will be in production. Apologies for inaccuracies in the graph overlay. I am trying to build an AI like that displayed in the diagram: when ...
5
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0answers
200 views

Wasserstein GAN: Implemention of Critic Loss Correct?

The WGAN paper concretely proposes Algorithm 1 (cf. page 8). Now, they also state what their loss for the critic and the generator is. When implementing the critic loss (so lines 5 and 6 of Algorithm ...
4
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1answer
67 views

GAN Generator Output w/ Periodic Noise

I am training a Semi-Supervised GAN, using multivariate time-series with window of shape (180*80) with the generator and discriminator architecture below. My data is scaled using Robust Scaler, so I ...
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2answers
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Using GAN's to generate dataset for CNN training

I'm doing bachaleor thesis on traffic sign detection using single shot detector called YOLO. These single shot detectors can perform detection of objects in image and so they have specific way of ...
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1answer
3k views

Why doesn't VAE suffer mode collapse?

Mode collapse is a common problem faced by GANs. I am curious why doesn't VAE suffer mode collapse?
4
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1answer
369 views

What kind of algorithm is used by StackGAN to generate realistic images from text?

What kind of algorithm is used by StackGAN to generate realistic images from text? How does StackGAN work?
4
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1answer
196 views

Why don't those developing AI Deepfake detectors use two detectors at once so as to catch deepfakes in one or the other?

Why don't those developing AI Deepfake detectors use two differently trained detectors at once that way if the Deepfake was trained to fool one of the detectors the other would catch it and vice-versa?...
4
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1answer
97 views

How can I use Generative Adversarial Networks to solve the imbalanced class problem?

Problem setting We have to do a binary classification of data given a training dataset $D$, where most items belong to class $A$ and some items belong to class $B$, so the classes are heavily ...
4
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1answer
186 views

How is G(z) related to x in GAN proof?

In the proofs for the original GAN paper, it is written: $$∫_x p_{data}(x) \log D(x)dx+∫_zp(z)\log(1−D(G(z)))dz =∫_xp_{data}(x)\log D(x)+p_G(x) \log(1−D(x))dx$$ I've seen some explanations asserting ...
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0answers
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Are generative models actually used in practice for industrial drug design?

I just finished reading this paper MoFlow: An Invertible Flow Model for Generating Molecular Graphs. The paper, which is about generating molecular graphs with certain chemical properties improved the ...
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0answers
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How many training data required for GAN?

I'm beginning to study and implement GAN to generate more dataset. I'll just try to experiment with state-of-the-art GAN models as described in here https://paperswithcode.com/sota/image-generation-on-...
3
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2answers
150 views

Mathematical Analysis of the Loss function of GAN

I was pondering on loss function of GAN and following thing turned out \begin{aligned} L(D, G) & = \mathbb{E}_{x \sim p_{r}(x)} [\log D(x)] + \mathbb{E}_{x \sim p_g(x)} [\log(1 - D(x)] \\ & =...
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3answers
746 views

Why use the output of the generator to train the discriminator in a GAN?

I've been doing some reading about GANs, and although I've seen several excellent examples of implementations, the descriptions of why those patterns were chosen isn't clear to me in many cases. At a ...
3
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2answers
580 views

What makes GAN or VAE better at image generation than NN that directly maps inputs to images

Say a simple neural network's input is a collection of tags (encoded in some way), and the output is an image that corresponds to those tags. Say this network consists of some dense layers and some ...
3
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1answer
209 views

What is the reason for mode collapse in GAN as opposed to WGAN?

In this article I am reading: $D_{KL}$ gives us inifity when two distributions are disjoint. The value of $D_{JS}$ has sudden jump, not differentiable at $\theta=0$. Only Wasserstein metric provides ...
3
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1answer
160 views

Why AI is (or not) a good option for the generation of random numbers?

Why AI is (or not) a good option for the generation of random numbers? Would GANs be suited for this purpose?
3
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1answer
1k views

What parameters can be tweaked to avoid a generator or discriminator loss collapsing to zero when training a DC-GAN?

Sometimes when I am training a DC-GAN on an image dataset, similar to the DC-GAN PyTorch example (https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html), either the Generator or ...
3
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1answer
56 views

How to define loss function for Discriminator in GANs?

To train the discriminator network in GANs we set the label for the true samples as $1$ and $0$ for fake ones. Then we use binary cross-entropy loss for training. Since we set the label $1$ for true ...
3
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1answer
82 views

How exactly does adversarial training help in handling mode-collapse in generative networks?

Of my understanding mode-collapse is when there happen to be multiple classes in the dataset and the generative network converges to only one of these classes and generates images only within this ...
3
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1answer
58 views

Why do we sample vectors from a standard normal distribution for the generator?

I am new to GANs. I noticed that everybody generates a random vector (usually 100 dimensional) from a standard normal distribution $N(0, 1)$. My question is: why? Why don't they sample these vectors ...
3
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0answers
39 views

Best Machine Learning Model for "Predicted" Image Generation

I am currently working on undergraduate research to determine hotspots for hand-surface contact. Ideally, I would like to give the model a depth image as input: Example of synthetic depth image and ...
3
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0answers
47 views

Is the GAN architecture better suited for medical image denoising than the CNN?

I'm considering using GANs for medical image denoising, based on previous literature, like this and this. My input to the GAN would be a high-noise image and my ideal output would be a low-noise, high-...
3
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1answer
814 views

Can someone explain R1 regularization function in simple terms?

I'm trying to understand the R1 regularization function, both the abstract concept and every symbol in the formula. According to the article, the definition of R1 is: It penalizes the discriminator ...
3
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0answers
36 views

How do GANs create an image with specific characteristics?

I've seen GANs that do things like convert an image to a painting or this GAN here https://make.girls.moe/#/ that takes in a set of characteristics and generates a waifu with those characteristics. ...
3
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2answers
379 views

How to use a Generative Adversarial Network to generate images for developmental analysis?

I want to generate images of childrens' drawings consistent with the developmental state of children of a given age. The training data set will include drawings made by real children in a school ...
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2answers
124 views

Recent deep learning textbook (i.e. covering at least GANs, LSTM and transformers and attention)

I am searching for an academic (i.e. with maths formulae) textbook which covers (at least) the following: GAN LSTM and transformers (e.g. seq2seq) Attention mechanism The closest match I got is Deep ...
2
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2answers
108 views

What are the possible social consequences of training neural networks with artificially generated data?

Machine learning models and, in particular, neural networks are trained with data often collected from the real world, such as images of real people. Meanwhile, neural networks (such as GANs) are also ...
2
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1answer
45 views

Have GANs been used to solve regression problems?

I've noticed that in the last 2 years GANs have become really popular. I know that initially they have been proposed for image classification but I was curious if any of you are aware of any papers ...
2
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1answer
31 views

Is it impossible to evaluate the generator distribution directly?

The following excerpt is taken from 3. The Inception Score for Image Generation from the paper titled A Note on the Inception Score. Suppose we are trying to ...
2
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1answer
88 views

Is discriminator a regressor or classifier in implementations?

GAN has two components: generator and discriminator. Discriminator in the original GAN is a regressor and always gives value in $[0, 1]$. You can read it in original paper $D(x)$ represents the ...
2
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1answer
68 views

In style transfer, why does the comparison between channels give a good sense of style?

I have been learning about Style Transfer recently. Style is defined as The correlation of activations between channels. I can't seem to understand why that would be true. Intuitively, style seems ...