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), the Generative Adversarial Network (GAN), Large Language Models (LLMs), and Diffusion Models.

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What are the advantages of GANs over Diffusion Models in image generation?

Diffusion Models have recently gained popularity in the field of image generation, with widely used products such as Stable Diffusion employing this approach and yielding impressive results. While ...
David's user avatar
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DDPM - why after adding gaussian noise to image, we assume that new image is from normal distribution?

I have a question about forward process in DDPM. It is described as we sample our image from some distribution: $x_0\sim{q(x)}$ then in each time stamp $T$ we are applying gaussian noise $\epsilon\sim\...
cvzx's user avatar
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Creating Technical Learning Assistant; should I finetune on the books and blog posts I want to learn from or do RAG on a vectordb?

I want to create a learning assistant agent to help me learn technical concepts faster. To that end I have a bunch of resources I believe would be helpful but I do not know if fine-tuning on the ...
D.Kiji_Noctis's user avatar
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If the unigram precision is (N-1)/N, then the bigram precision is :

Consider the following machine translation scenario. The reference translation has N words (do not consider sentence beginner ‘hat’ and sentence finisher ‘dot’). The machine output also has N words. ...
Geeklovenerds's user avatar
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Can higher order derivatives be used in diffusion models?

I remember reading an article about predicting both the first and second derivative in a diffusion model and then using the Taylor method to denoise in each step, to achieve faster convergence. But I ...
Sartem Cacartem's user avatar
1 vote
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Pointers to (deep) latent variable models that admit analytical approximations

I am aware that there is a plethora of deep generative models out there (e.g. variational autoencoders (VAE), GANs) that can model high-dimensional data as the images of latent variables under a non-...
ngiann's user avatar
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VAE for Motion Sequence Generation - Convergence Issue with Scheduled Sampling

I have implemented a Variational Autoencoder (VAE) in PyTorch for motion sequence generation using human pose data (joint angles and angular velocities in radians) from the CMU dataset. The VAE ...
RTn's user avatar
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How does diffusion model (DDPM) ensures novel generated samples?

I am trying to understand the theoretical aspect of the denoising diffusion model. There we try to destroy the initial image x_0 through a chain of forward process and then learn a backward diffusion ...
Formal_this's user avatar
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20 views

Training Diffusion Probabilistic Models

My question is in regards to the training process for Diffusion Probabilistic Models by Sohl-Dickenstein et. al. and also Ho et. al. and this blog post For the derivation of the model log-likelihood ...
diffusionQuestion's user avatar
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What is the detailed experimental setup for class-incremental continual image generation?

Do you condition the generative model (let's say, VAE) on the task identity or the class label or both? If I condition the VAE on both task identity and class label, then I have to provide both the ...
Homie98's user avatar
1 vote
1 answer
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What does "fully visible belief network" stand for?

In CS231n, I saw the terminology "fully visible belief network", one category of explicit density generative model such as PixelCNN and NADE. Although I can understand what this terminology ...
Chris's user avatar
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Where to find all the documentation regarding StableDiffusion's special prompts (like mixing two persons)

I am currently playing on ClipDrop.co with Stable Diffusion, and I am confused with the tutorials I can find online. For instance, for mixing two celebrities in one pics, I read all the following ...
Myoch's user avatar
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How to generate quality synthetic images of human subjects to be used for training stable diffusion

I'm trying to generate some synthetic images of humans, that are "good enough" to be used to fine-tune a stable diffusion model, but i'm not sure if this is possible. I have experimented ...
interesting's user avatar
1 vote
1 answer
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What is x, y, p(x), p(y) in generative model domain?

Background Generative modeling Generative modeling aims to model the probability of observing an observation x. $$ p(x) = \frac{p(y\cap x)}{p(y|x)} $$ ...
Prakhar's user avatar
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1 answer
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Justification of Scaling in Classifier-Free Guidance in Diffusion Models

Background | Classifier-Free Guidance Derivation To summarize the derivation of Classifier-Free Guidance, looking at this paper (Page 21.), we can write classifier guidance as: $$\nabla_{x}\log p\...
snatchysquid's user avatar
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Small language model vocabulary correction

I plan to train a small language model (less than 4B parameters) that can run on x86 and handle vocabulary correction such as: ...
Jeff Brower's user avatar
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1 answer
75 views

how the GAN architecture maintain similar images close in the latent space?

I am learning about generative models, and I don't quite understand how the GAN architecture can maintain similar generated images close in the latent space. For example, an autoencoder and a ...
Cesar Ruiz's user avatar
1 vote
2 answers
85 views

Maximize a scoring function within the latent space of a generative model

Given a generative model, G, trained on a dataset D. This generative model can be either GAN or Diffusion based. Supposed each sample, x_i, generated by G, can be evaluated by a readily available ...
terenceflow's user avatar
1 vote
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37 views

Can back-bone of text-to-image GEN AI models utilised for classification?

With the advent of GEN AI (Stable Diffusion), we are able to create images with text. For eg. If i need to create a dog on beach during sunset; now in background this model needs to first get images ...
prat__'s user avatar
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Can I sample finite or infinite images with AutoRegressive Models?

I'm learning about AutoRegressive Models used on images, and I've studied the training phase, where you model each pixel on the basis of the previous ones using a certain model architecture. My ...
SuperFluo's user avatar
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Can AI-written text detection be made more accurate if you know the prompt?

Usually, genAI detection is of the form: Input: text. Output: was it generated by AI? Thus far, AI-written text detection is terribly inaccurate, and if you're a user of r/ChatGPT, you've probably ...
Rebecca J. Stones's user avatar
1 vote
1 answer
98 views

Is there a limitation to the amount of data that a genAI model could be trained upon?

My friend says that genAI would become more human like, and perhaps even smarter than humans if it were simply trained on more and more data. I say that this would overtrain the models, and we would ...
tryst with freedom's user avatar
2 votes
1 answer
92 views

Could hallucinations be the demise of the AI hype? [closed]

For quite some time now, I have been evaluating ChatGPT's capability to deliver accurate and helpful responses. While its performance is undeniably impressive, the issue of hallucinations poses a ...
machine_1's user avatar
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What's the architecture that allows the generation of new images based on input image in tools like Midjourney?

I understand that the high-level architecture of tools like Midjourney use diffusion models to generate images from text. What I don't understand is which type of network architecture allow the second ...
emilaz's user avatar
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KL divergence and sign [duplicate]

In the Auto-Encoding Variational Bayes paper, the formula for KL divergence is $$ \frac{1}{2} \sum \bigl (1 + \log(σ^2) - μ^2 - σ^2 \bigr) \space\space\space\space...(10)$$ , but the equation is $$- ...
diffusion stable's user avatar
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How far is AI from making movies just by using script or description like we use for generating images in midjourney or DALL-E etc

I'm curious about the current state of AI technology when it comes to generating movies from textual descriptions. I'm aware of impressive advancements in generating images using models like ...
طلحة's user avatar
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2 votes
2 answers
2k views

About cosine noise schedule in Diffusion Model

Could you provide the explanation of Figure 4 from the paper at Improved Denoising Diffusion Probabilistic Models? (1) The paper says, "the end of the forward noising process is too noisy,and so ...
diffusion stable's user avatar
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1 answer
119 views

Could generative AI bootstrap?

In programming, if a new language could be improved by the language itself, it’s call self-hosting or bootstrapping. To develop generative AI, there’s some steps, data preparing, model training, fine ...
zzzgoo's user avatar
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Diffusion Models - Loss normalization for different noise schedules

With reference to the paper "Denoising Diffusion Probabilistic Models", and running the code given in this notebook showing a simple example on "flattened" data, I have the ...
Peblo's user avatar
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Resources to understand the math behind diffusion models

I started learning about diffusion models, but I couldn't follow the math proofs (probability distribution terms and other stuff), is there any sources you suggest to understand what they're talking ...
hakim47's user avatar
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What's the right approach to "teach" a chatbot about a big specific data set: training it or feeding it the input once?

I would like to build a chatbot that I can talk to about any specific data set I provide and I have a theoretical question. Let's say, I want to discuss French cuisine. This could involve providing ...
lesssugar's user avatar
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2 votes
1 answer
52 views

Clarification on the training objective of denoising diffusion models

I'm reading the Denoising Diffusion Probabilistic Models paper (Ho et al. 2020). And I am puzzled about the training objective. I understood (I think) the trick regarding the reparametrization of the ...
user3903647's user avatar
0 votes
1 answer
99 views

Blurring of image in generative model using diffusion probabilistic method

In the 2015 paper "Deep Unsupervised Learning using Nonequilibrium Thermodynamics" by Sohl-Dickstein et al. on diffusion for generative models, Figure 1 shows the forward trajectory for a 2-...
sunfishstanford's user avatar
3 votes
0 answers
194 views

Reverse Process in Diffusion Model Doesn't Return Original Image

I am attempting to program a Denoising Diffusion Model based on the one introduced in the article by Ho et al. (2020). However, I have run into issues while testing the reverse diffusion process. ...
cabralpinto's user avatar
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1 answer
107 views

Keypoint generation in 3D point clouds with Deep Learning

I have a huge dataset of 3D point clouds (each point consists of X,Y,Z coordinates) and another dataset with keypoints (also X,Y,Z) which lie on quite recognizable structures in the point cloud. As a ...
nmb's user avatar
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1 vote
0 answers
88 views

Coding a conversational AI which remembers previous context

I am trying to code a proper conversational AI which remembers previous context and answers accordingly (something like a micro ChatGPT). Additionally I want the AI to work on a custom knowledge base ...
JAYDEEP GHOSE's user avatar
2 votes
2 answers
2k views

What makes ChatGPT a generative model?

I'm working my way through how ChatGPT works. So I read that ChatGPT is a generative model. When searching for generative models, I found two defintions: A generative model includes the distribution ...
Ai4l2s's user avatar
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1 vote
1 answer
152 views

What is the difference between the term "generative" in classical machine learning and deep learning?

There are lots of explanations on DGM (Deep Generative Model) and generative classifier (most of the explanations on which are about generative classifier vs discriminative classifier) But, I can ...
Rhee's user avatar
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0 answers
17 views

Idea for generating time series with irregular time-intervals with GANs

I want to model time-series with irregular time-intervals using GANs. Think of the following (short) data sample ...
Robin van Hoorn's user avatar
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1 answer
187 views

How to generate new data using VAE?

I have built the following function which takes as input some data and runs a VAE on them: ...
quant's user avatar
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1 vote
2 answers
111 views

Reverse Distribution in Denoising Diffusion Models is Simple

In explanations of denoising diffusion models it is stated that $q(x_{t-1}|x_t)$ is intractable. This is often justified via Bayes' rule, i.e. $$ q(x_{t-1}|x_t) \propto q(x_t|x_{t-1})q(x_{t-1}) $$ and ...
Patrick Johnstone's user avatar
1 vote
0 answers
136 views

Is it possible to combine DDPM with GAN?

From what I understand in GAN, the main idea is that you have a generator and a discriminator network that are "competing" with each other. The generator trying to make images that the ...
Nikita Belooussov's user avatar
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0 answers
13 views

How does the 'moment's matching trick' in GAN training improve the diversity of the generated samples?

I was investigating the TimeGAN code, when i stumbled across the 'moments loss' component. In one of the issues, the author states that this is a 'moment's matching trick' used 'to improve the ...
Robin van Hoorn's user avatar
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24 views

Adjusting weight updates in a generative model

Suppose I am training a generative model G to produce vectors z in R^d, where d is fixed. The objective of G is to produce realistic vectors, which I am calling the "reality objective". ...
postnubilaphoebus's user avatar
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38 views

Why would one still use a traditional GAN architecture or WGAN architecture instead of a WGAN-GP architecture?

I've been diving into the literature of GANs, and quite early on, I was pretty convinced that WGAN-GPs were the way to go. The WGAN-GP architecture is, as far as I know, theoretically and empirically ...
Robin van Hoorn's user avatar
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0 answers
15 views

Adding MNIST images by using them as channel inputs

I'm trying to create a generative neural network that can offer "basic sum" mathematical solutions using the MNIST dataset from a conditional input. I've curated a dataset of MNIST examples ...
Zintho's user avatar
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-1 votes
1 answer
499 views

Is Diffusion model instable during the training? [closed]

I ran an experiment using a diffusion model (SR3 model) and got good results (experiment using jupyter notebook). Then, after running the same notebook many times with the same configuration, I did ...
ProEns08's user avatar
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1 vote
0 answers
37 views

Can a convolution learn to generate fine details? [closed]

I'm trying to get a convolutional autoencoder to reconstruct images of a dataset with crisp details. I've read in a couple places that convolutional autoencoders "naturally produce blurry images&...
Soltius's user avatar
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What are common benchmarks (simulators and datasets) for testing compositionally in the visual domain?

Learning an object decomposition from a visual scene is a difficult problem for language models describing the scene, for radiance fields that reconstruct the scene, and also for generative models ...
Mariusmarten's user avatar
1 vote
1 answer
87 views

How is the variance for a diffusion kernel derived for a diffusion model?

So I'm watching this video tutorial from CVPR this year on diffusion models, and I am confused by the variance term in the distribution on the left on the video. I understand that in the forward ...
Cynthia Kim's user avatar