Questions tagged [generative-model]
For questions related to the concept of generative machine learning models, such as the Restricted Boltzmann Machine (RBM), the Variational Autoencoder (VAE), and the Generative Adversarial Network (GAN).
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21 views
Do I have to downsample the input and upsample the output of the neural network when implementing the NICE algorithm?
Consider that my input is an RGB image. The size of my image is $N\times N$. I'm trying to implement NICE algorithm presented by Dinh. The bijective function $f: \mathbb{R}^d \to \mathbb{R}^d$ maps $X$...
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0answers
17 views
Is there any way of generating fixed-length sequences with RNNs?
Is there any way of generating fixed-length sequences with RNNs? I want to tell my character level RNN to generate a name of length 3, 4, 5 and so on. I haven't found anything online like this, but my ...
2
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0answers
33 views
Should the RL agent be trained in an environment with real-world data or with a synthetic model?
I want to train a reinforcement learning agent in an environment with parameters (for example, the wind speed, sun irradiation, etc.) that change over time. I have recorded a limited amount of data ...
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0answers
37 views
Corporation mechanism to simulate the christian Religion psychic model and build AI Algorithms [closed]
I was reading some religion text and seen some world like faith which i think is designed to give a path for blind system to find the best fit function during it's turns, so this question came in my ...
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1answer
21 views
GANs: Should Generator update weights when Discriminator says false continuously
My GANs is like this:
Train an autoencoder (VAE), get the decoder part and use as Generator
Train Discriminator
After training, do the generation in these steps:
Call Generator to generate an image
...
2
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0answers
26 views
Suggestion on image inpainting algorithm
Currently, many algorithms are available for image inpainting. In my application, I have some special restriction on training dataset-
Let's consider the training dataset of human facial images.
...
1
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0answers
20 views
Which generative methods are better for generating graphs, while preserving node and edge labels?
I started to dig into the topic of graph generation and I have a question - which out of generative methods (autoregressive, variational autoencoders, GANs, any other?) are better for generating ...
2
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2answers
40 views
Is it feasible to use GAN for high-quality image synthesis other than human faces?
The famous Nvidia paper Progressive Growing of GANs for Improved Quality, Stability, and Variation, the GAN can generate hyperrealistic human faces. But, in the very same paper, images of other ...
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0answers
18 views
What is the need for Auxiliary Decoder in the VAE-GAN?
The below image taken from Tim Sainberg's GitHub repo (https://github.com/timsainb) shows the structure of a VAE-GAN:
My question is about the second row in the diagram. Random samples drawn from z ...
3
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0answers
25 views
Creating an AI than can learn to give instructions
So we think a computer is dumb because it can only follow instructions. Therefor I am trying to create an AI that can give instructions.
The idea is this: Create a geometric scene (A) then make a ...
3
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0answers
115 views
Speaker Identification / Recognition for less size audio files
I am working on speaker identification problem using GMM (Gaussian Mixture Model). I have to just identify one user present in the given audio, so for second class noise or silent audio may use or not ...
2
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0answers
7 views
Improving graph decoder network
I have been using a network to generate graphs. The architecture that I have been using is the following:
In this figure, $D_1$ is the signal generator and $D_2$ is the graph topology generator, ...
2
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0answers
29 views
What is the input for the prior model of VQ-VAE?
I'm trying to implement the VQ-VAE model. In there, a continuous variable $x$ is encoded in an array $z$ of discrete latent variables $z_i$ that are mapped each to an embedding vector $e_i$. These ...
2
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1answer
52 views
Giving an AI a purpose to talk
I am trying to teach my AI to talk. The problem is I'm struggling to find a good scenario in which it needs to.
Some ideas I had were:
"Describe a geometric scene" - Then together with a parser we ...
3
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0answers
49 views
Does MMD-VAE solve the problem of blurred images of vanilla VAEs?
I understand that with vanilla VAEs, there are a few reasons justifying the production of blurred out images. The InfoVAE paper describes the case when the decoder is flexible enough to ignore the ...
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0answers
27 views
Are there any general tips for troubleshooting a VAE when apparently it is not learning?
I am trying to train a VAE for anomaly detection. I chose one architecture from this Github repository and I adjusted the input and output to match what I need. In my case, the input (and hence the ...
5
votes
1answer
81 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 ...
2
votes
1answer
44 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 ...
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0answers
29 views
What's a good generative model for creating valid formats of a person's name?
I'm trying to come up with a generative model that can input a name and output all valid formats of it.
For example, "Bob Dylan" could be an input and the gen model will output "Dylan, Bob", "B ...
0
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1answer
21 views
How to change this RNN text classification code to become text generation code?
I can do text classification with RNN, in which the last output of RNN (rnn_outputs[-1]) is used to matmul with output layer weight and plus bias. That is getting a word (class name) after the last T ...
3
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2answers
36 views
How can we find find the input image which maximizes the class-probability for an ANN?
Let's assume we have an ANN which takes a vector $x\in R^D$, representing an image, and classifies it over two classes. The output is a vector of probabilities $N(x)=(p(x\in C_1), p(x\in C_2))^T$ and ...
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0answers
12 views
Wasserstein GAN with non-negative weights in the critic
I want to train a WGAN where the convolution layers in the critic are only allowed to have non-negative weights (for a technical reason). The biases, nonetheless, can take both +/- values. There is no ...
3
votes
1answer
132 views
Which approach can I use to generate text based on multiple inputs?
I have a little experience in building various models, but I've never created anything like this, so just wondering if I can be pointed in the right direction.
I want to create (in python) a model ...
2
votes
1answer
78 views
What is the purpose of the noise injection in the generator network of a GAN?
I do not understand why with enough training how the generator cannot learn all images from the training set as a mapping from the latent space - It is the absolute optimal case in training as it ...
0
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1answer
149 views
Query regarding the minmax loss function formulation of the training of a Generative Adversarial Network (GAN)
Just needed a clarification on the training procedure for a standard GAN.
Of my understanding the loss function to optimize is a min max (max min causing mode collapse due to focus on one class ...
0
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1answer
59 views
If the goal of training of a GAN is to have $P_g=P_{data}$, shouldn't this produce the exact same images?
Referring to the blog, Image Completion with Deep Learning in TensorFlow, it clearly says that we would want a generator $g$ whose modeled distribution fits our dataset $data$, in other words, $P_{...
3
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1answer
24 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 ...
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0answers
23 views
How to replay an event-detection model?
Event recognition [1] is a technique to identify in a video stream a semantic action, for example “start walking”. The pixels on the screen are doing something, and the activity parser recognizes the ...
2
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0answers
38 views
Can GANs be used to generate matching pairs to inputs?
I have some limited experience with MLPs and CNNs. I am working on a project where I've used a CNN to classify "images" into two classes, 0 and 1. I say "images" as they are not actually images in the ...
2
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1answer
70 views
Other deep learning image generation techniques besides GANs?
Can you please point me to some resources about image genereation besides GANs?
Are there any other techniques throughout history?
How did idea of image generation evolved and how it started?
I tried ...
2
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0answers
23 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.
...
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0answers
31 views
Which libraries can be used for image caption generation?
Which libraries can be used for image caption generation?
2
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2answers
100 views
How important is it that the generator of a generative adversarial network doesn't take in information about input classes?
I'm building a generative adversarial network that generates images based on an input image. From the literature I've read on GANs, it seems that the generator takes in a random variable and uses it ...
6
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1answer
376 views
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 ...
3
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0answers
25 views
How do we ensure that training GANs will fall in the desirable Nash equilibrium?
One Nash equilibrium of every GANs model has is when the generator creates perfect samples indistinguishable from the training data and the discriminator just output 1 with probability 1/2. And I ...
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1answer
115 views
How to get good results with GAN and some thousands of images?
I'm trying to generate images at minimum of size 128 x 128 with a Generative Adversarial Network. I already tried a SAGAN pytorch implementation, but I'm not very happy with results. The images look ...
3
votes
2answers
104 views
Do we also need to model a probability distribution for the decoder of a VAE?
I'm working on understanding VAEs, mostly through video lectures of Stanford cs231n, in particular lecture 13 tackles on this topic and I think I have a good theoretical grasp.
However, when looking ...
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1answer
21 views
What is the goal of the model and is the training data relevant to that?
The model that we develop in artificial intelligence.What is its purpose,and what training data is relevant to it.
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0answers
58 views
How do PGMs factor in to modern ML?
I just finished the three-part series of Probabilistic Graphical Models courses from Stanford over on Coursera. I got in to them because I realized there is a certain class of problem for which the ...
2
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3answers
732 views
Confusing on GAN loss function
I was trying to understand the loss function of GANs, while I found a little mis-match between different papers.
This is the screen-shot from the original paper of Goodfellow at https://arxiv.org/...
3
votes
1answer
101 views
Why is the last layer of a DBN or DBM used for classification task?
I understand why deep generative models like DBN ( deep belief nets ) or DBM ( deep boltzmann machines ) are able to capture underlying structures in data and use it for various tasks ( ...
2
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1answer
67 views
Is there a mathematical example for Conditional Random Fields?
I am learning about probabilistic graphical models and I was wondering if there is an example explaining the math behind conditional random fields. Looking solely on the formula, I have no idea what ...
2
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0answers
50 views
What kind of distributions can be used to model discrete latent variables?
If we take the vanilla variational auto-encoder (VAE), we $p(z)$ is a Gaussian distribution with zero mean and unit variance and we approximate $p(z|x) \approx q(z|x)$ to be a Gaussian distribution as ...
4
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2answers
2k views
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 ...
4
votes
1answer
55 views
Class Restriction in Generative Adversarial Networks
this is my first post here.
Our problem setting:
We have to do a binary classification of data given a training-dataset D, where the majority of items belongs to class A and some items belong to ...
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0answers
184 views
Stack for Automatic 3D Mesh Generation
Gist: Should I use LISP for a part of the following project. What are the other options.
Me and a friend are planning to create a 3D Modelling Agent where a designer can :-
Specify constrains on how ...
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0answers
19 views
Adding voices to voice synthesis corpuses
If one uses one of the open source implementations of the WaveNet generative speech synthesis design, such as https://r9y9.github.io/wavenet_vocoder/, and trains using something like the CMU's arctic ...
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1answer
84 views
How can AI be used to more reliably analyze and plan around the tie between climate and emissions?
Note to the Duplicate Police
This question is not a duplicate of the Q&A thread referenced in the close request. The only text even remotely related in that other thread is the brief mention of ...
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0answers
386 views
Why do we need Upsampling and Downsampling in Progressive Growing of Gans
I was working recently on Progressive Growing of GANs (aka PGGANs). I have implemented the whole architecture, but the problem that was ticking my mind is that in simple GANs, like DCGAN, PIX2PIX, we ...
4
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1answer
396 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 ...
